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Python: Real-World Data Science

You're reading from   Python: Real-World Data Science Real-World Data Science

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Product type Course
Published in Jun 2016
Publisher
ISBN-13 9781786465160
Length 1255 pages
Edition 1st Edition
Languages
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Authors (5):
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Fabrizio Romano Fabrizio Romano
Author Profile Icon Fabrizio Romano
Fabrizio Romano
Phuong Vo.T.H Phuong Vo.T.H
Author Profile Icon Phuong Vo.T.H
Phuong Vo.T.H
Robert Layton Robert Layton
Author Profile Icon Robert Layton
Robert Layton
Sebastian Raschka Sebastian Raschka
Author Profile Icon Sebastian Raschka
Sebastian Raschka
Martin Czygan Martin Czygan
Author Profile Icon Martin Czygan
Martin Czygan
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Table of Contents (12) Chapters Close

Table of Contents FREE CHAPTER
Python: Real-World Data Science
Meet Your Course Guide
What's so cool about Data Science?
Course Structure
Course Journey
The Course Roadmap and Timeline
1. Course Module 1: Python Fundamentals 2. Course Module 2: Data Analysis 3. Course Module 3: Data Mining 4. Course Module 4: Machine Learning Index

Chapter 9. Authorship Attribution

Authorship analysis is, predominately, a text mining task that aims to identify certain aspects about an author, based only on the content of their writings. This could include characteristics such as age, gender, or background. In the specific authorship attribution task, we aim to identify who out of a set of authors wrote a particular document. This is a classic case of a classification task. In many ways, authorship analysis tasks are performed using standard data mining methodologies, such as cross fold validation, feature extraction, and classification algorithms.

In this chapter, we will use the problem of authorship attribution to piece together the parts of the data mining methodology we developed in the previous chapters. We identify the problem and discuss the background and knowledge of the problem. This lets us choose features to extract, which we will build a pipeline for achieving. We will test two different types of features: function...

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