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Scala for Data Science

You're reading from   Scala for Data Science Leverage the power of Scala with different tools to build scalable, robust data science applications

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Product type Paperback
Published in Jan 2016
Publisher
ISBN-13 9781785281372
Length 416 pages
Edition 1st Edition
Languages
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Author (1):
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Pascal Bugnion Pascal Bugnion
Author Profile Icon Pascal Bugnion
Pascal Bugnion
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Table of Contents (17) Chapters Close

Preface 1. Scala and Data Science FREE CHAPTER 2. Manipulating Data with Breeze 3. Plotting with breeze-viz 4. Parallel Collections and Futures 5. Scala and SQL through JDBC 6. Slick – A Functional Interface for SQL 7. Web APIs 8. Scala and MongoDB 9. Concurrency with Akka 10. Distributed Batch Processing with Spark 11. Spark SQL and DataFrames 12. Distributed Machine Learning with MLlib 13. Web APIs with Play 14. Visualization with D3 and the Play Framework A. Pattern Matching and Extractors Index

Spam filtering


Let's put all we've learned to good use and do some data exploration for our spam filter. We will use the Ling-Spam email dataset: http://csmining.org/index.php/ling-spam-datasets.html. The dataset contains 2412 ham emails and 481 spam emails, all of which were received by a mailing list on linguistics. We will extract the words that are most informative of whether an email is spam or ham.

The first steps in any natural language processing workflow are to remove stop words and lemmatization. Removing stop words involves filtering very common words such as the, this and so on. Lemmatization involves replacing different forms of the same word with a canonical form: both colors and color would be mapped to color, and organize, organizing and organizes would be mapped to organize. Removing stop words and lemmatization is very challenging, and beyond the scope of this book (if you do need to remove stop words and lemmatize a dataset, your go-to tool should be the Stanford NLP toolkit...

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