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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

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Product type Paperback
Published in Apr 2017
Publisher Packt
ISBN-13 9781787126787
Length 358 pages
Edition 2nd Edition
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Concepts
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Toc

Table of Contents (14) 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. 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...

Extracting the blog posts

We are first going to create a MapReduce program that will extract each of the posts from each blog file and store them as separate entries. As we are interested in the gender of the author of the posts, we will extract that too and store it with the post.

We can't do this in a Jupyter Notebook, so instead open a Python IDE for development. If you don't have a Python IDE you can use a text editor. I recommend PyCharm, although it has a larger learning curve and it is probably a bit heavy for just this chapter's code.

At the very least, I recommend using an IDE that has syntax highlighting and basic completion of variable names (that last one helps find typos in your code easily.

If you still can't find an IDE you like, you can write the code in an IPython Notebook and then click on File| Download As| Python. Save this file to a directory and run it as we outlined in...
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