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IPython Interactive Computing and Visualization Cookbook

You're reading from   IPython Interactive Computing and Visualization Cookbook Harness IPython for powerful scientific computing and Python data visualization with this collection of more than 100 practical data science recipes

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Product type Paperback
Published in Sep 2014
Publisher
ISBN-13 9781783284818
Length 512 pages
Edition 1st Edition
Languages
Tools
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Author (1):
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Cyrille Rossant Cyrille Rossant
Author Profile Icon Cyrille Rossant
Cyrille Rossant
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Toc

Table of Contents (17) Chapters Close

Preface 1. A Tour of Interactive Computing with IPython FREE CHAPTER 2. Best Practices in Interactive Computing 3. Mastering the Notebook 4. Profiling and Optimization 5. High-performance Computing 6. Advanced Visualization 7. Statistical Data Analysis 8. Machine Learning 9. Numerical Optimization 10. Signal Processing 11. Image and Audio Processing 12. Deterministic Dynamical Systems 13. Stochastic Dynamical Systems 14. Graphs, Geometry, and Geographic Information Systems 15. Symbolic and Numerical Mathematics Index

Learning from text – Naive Bayes for Natural Language Processing


In this recipe, we show how to handle text data with scikit-learn. Working with text requires careful preprocessing and feature extraction. It is also quite common to deal with highly sparse matrices.

We will learn to recognize whether a comment posted during a public discussion is considered insulting to one of the participants. We will use a labeled dataset from Impermium, released during a Kaggle competition.

Getting ready

Download the Troll dataset from the book's GitHub repository at https://github.com/ipython-books/cookbook-data.

This dataset was obtained from Kaggle, at www.kaggle.com/c/detecting-insults-in-social-commentary.

How to do it...

  1. Let's import our libraries:

    In [1]: import numpy as np
            import pandas as pd
            import sklearn
            import sklearn.cross_validation as cv
            import sklearn.grid_search as gs
            import sklearn.feature_extraction.text as text
            import sklearn.naive_bayes as nb...
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