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Learning Data Mining with Python

You're reading from Learning Data Mining with Python Use Python to manipulate data and build predictive models

Product type Paperback

Published in Apr 2017

Publisher Packt

ISBN-13 9781787126787

Length 358 pages

Edition 2nd Edition

Languages

Python

Concepts

Data Mining

Table of Contents (14) Chapters

Preface

1. Getting Started with Data Mining FREE CHAPTER

2. Classifying with scikit-learn Estimators

3. Predicting Sports Winners with Decision Trees

4. Recommending Movies Using Affinity Analysis

5. Features and scikit-learn Transformers

6. Social Media Insight using Naive Bayes

7. Follow Recommendations Using Graph Mining

8. Beating CAPTCHAs with Neural Networks

9. Authorship Attribution

10. Clustering News Articles

11. Object Detection in Images using Deep Neural Networks

12. Working with Big Data

13. Next Steps...

Authorship Attribution

When it comes to Authorship Attribution do give the following topics a read.

Increasing the sample size

The Enron application we used ended up using just a portion of the overall dataset. There is lots more data available in this dataset. Increasing the number of authors will likely lead to a drop in accuracy, but it is possible to boost the accuracy further than was achieved here, using similar methods. Using a Grid Search, try different values for n-grams and different parameters for support vector machines, in order to get better performance on a larger number of authors.

Blogs dataset

The dataset used, provides authorship-based classes (each blogger ID is a separate author). This dataset can be tested using this kind of method as well. In addition, there are the other classes of gender, age, industry, and star sign that can be tested—are authorship-based methods good for these classification tasks?

Local n-grams

URL: https://github.com/robertlayton/authorship_tutorials...

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