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Practical Data Analysis

You're reading from  Practical Data Analysis

Product type Book
Published in Oct 2013
Publisher Packt
ISBN-13 9781783280995
Pages 360 pages
Edition 1st Edition
Languages
Author (1):
Hector Cuesta Hector Cuesta
Profile icon Hector Cuesta
Toc

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 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

Summary


In this chapter, we created a basic but useful e-mail subject line tester. This chapter provided a guide to code a basic Naïve Bayes classifier from scratch without any external library, in order to demonstrate how easy it is to program a machine-learning algorithm. We also defined the maximum size threshold for the training set and got an accuracy of 92 percent, which, for this basic example, is quite good.

In the following chapters, we will introduce more complex machine learning algorithms, using the mlpy library, and we will also present how to extract more sophisticated features.

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