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Practical Data Analysis

You're reading from   Practical Data Analysis For small businesses, analyzing the information contained in their data using open source technology could be game-changing. All you need is some basic programming and mathematical skills to do just that.

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Product type Paperback
Published in Oct 2013
Publisher Packt
ISBN-13 9781783280995
Length 360 pages
Edition 1st Edition
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Author (1):
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Hector Cuesta Hector Cuesta
Author Profile Icon Hector Cuesta
Hector Cuesta
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Table of Contents (24) Chapters Close

Practical Data Analysis
Credits
Foreword
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Preface
1. Getting Started FREE CHAPTER 2. Working with Data 3. Data Visualization 4. Text Classification 5. Similarity-based Image Retrieval 6. Simulation of Stock Prices 7. Predicting Gold Prices 8. Working with Support Vector Machines 9. Modeling Infectious Disease with Cellular Automata 10. Working with Social Graphs 11. Sentiment Analysis of Twitter Data 12. Data Processing and Aggregation with MongoDB 13. Working with MapReduce 14. Online Data Analysis with IPython and Wakari Setting Up the Infrastructure Index

E-mail subject line tester


An e-mail subject line tester is a simple program, which will define if a certain subject line in an e-mail is spam or not. In this chapter, we will program a Naïve Bayes classifier from scratch. The example will classify if a subject line is spam or not using a very simple code. This will be done by breaking the subject lines into a list of relevant words, which will be used as the features vectors in the algorithm. In order to do this, we will use the SpamAssassin public dataset. SpamAssasin includes three categories; spam, easy ham, and hard ham. In this case, we will create a binary classifier with two classes spam and not spam (easy ham).

There are several features that we can use for our classifier such as the precedence, the language, and the use of upper case. We will keep things simple and use the frequency of only those words which consist of more than three characters, avoiding words such as The or RT, when training the algorithm.

We will implement the...

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