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Learning Data Mining with Python

You're reading from   Learning Data Mining with Python Harness the power of Python to analyze data and create insightful predictive models

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Product type Paperback
Published in Jul 2015
Publisher Packt
ISBN-13 9781784396053
Length 344 pages
Edition 1st Edition
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Author (1):
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Robert Layton Robert Layton
Author Profile Icon Robert Layton
Robert Layton
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Table of Contents (15) Chapters Close

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. Extracting Features with Transformers 6. Social Media Insight Using Naive Bayes 7. Discovering Accounts to Follow Using Graph Mining 8. Beating CAPTCHAs with Neural Networks 9. Authorship Attribution 10. Clustering News Articles 11. Classifying Objects in Images Using Deep Learning 12. Working with Big Data A. Next Steps… Index

Using the Enron dataset


Enron was one of the largest energy companies in the world in the late 1990s, reporting revenue over $100 billion. It has over 20,000 staff and—as of the year 2000—there seemed to be no indications that something was very wrong.

In 2001, the Enron Scandal occurred, where it was discovered that Enron was undertaking systematic, fraudulent accounting practices. This fraud was deliberate, wide-ranging across the company, and for significant amounts of money. After this was publicly discovered, its share price dropped from more than $90 in 2000 to less than $1 in 2001. Enron shortly filed for bankruptcy in a mess that would take more than 5 years to finally be resolved.

As part of the investigation into Enron, the Federal Energy Regulatory Commission in the United States made more than 600,000 e-mails publicly available. Since then, this dataset has been used for everything from social network analysis to fraud analysis. It is also a great dataset for authorship analysis...

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