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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

Feature extraction


Extracting features is one of the most critical tasks in data mining, and it generally affects your end result more than the choice of data mining algorithm. Unfortunately, there are no hard and fast rules for choosing features that will result in high performance data mining. In many ways, this is where the science of data mining becomes more of an art. Creating good features relies on intuition, domain expertise, data mining experience, trial and error, and sometimes a little luck.

Representing reality in models

Not all datasets are presented in terms of features. Sometimes, a dataset consists of nothing more than all of the books that have been written by a given author. Sometimes, it is the film of each of the movies released in 1979. At other times, it is a library collection of interesting historical artifacts.

From these datasets, we may want to perform a data mining task. For the books, we may want to know the different categories that the author writes. In the films...

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