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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

A simple classification example

In the affinity analysis example, we looked for correlations between different variables in our dataset. In classification, we instead have a single variable that we are interested in and that we call the class (also called the target). If, in the previous example, we were interested in how to make people buy more apples, we could set that variable to be the class and look for classification rules that obtain that goal. We would then look only for rules that relate to that goal.

You have been reading a chapter from
Learning Data Mining with Python
Published in: Jul 2015
Publisher: Packt
ISBN-13: 9781784396053
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