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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
Languages
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...

Putting it all together


We can now actually run the Naive Bayes classifier using these probabilities. We will do this in a Jupyter Notebook, although this processing itself can be transferred to a mrjob package to be performed at scale.

First, take a look at the models folder that was specified in the last MapReduce job. If the output was more than one file, we can merge the files by just appending them to each other using a command line function from within the models directory:

cat * > model.txt

If you do this, you'll need to update the following code with model.txt as the model filename.

Back to our Notebook, we first import some standard imports we need for our script:

import os 
import re
import numpy as np 
from collections import defaultdict 
from operator import itemgetter

We again redefine our word search regular expression—if you were doing this in a real application, I recommend centralizing the functionality. It is important that words are extracted in the same way for both training...

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