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Python Machine Learning By Example

You're reading from   Python Machine Learning By Example The easiest way to get into machine learning

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Product type Paperback
Published in May 2017
Publisher Packt
ISBN-13 9781783553112
Length 254 pages
Edition 1st Edition
Languages
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Authors (2):
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Yuxi (Hayden) Liu Yuxi (Hayden) Liu
Author Profile Icon Yuxi (Hayden) Liu
Yuxi (Hayden) Liu
Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
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Table of Contents (9) Chapters Close

Preface 1. Getting Started with Python and Machine Learning FREE CHAPTER 2. Exploring the 20 Newsgroups Dataset with Text Analysis Algorithms 3. Spam Email Detection with Naive Bayes 4. News Topic Classification with Support Vector Machine 5. Click-Through Prediction with Tree-Based Algorithms 6. Click-Through Prediction with Logistic Regression 7. Stock Price Prediction with Regression Algorithms 8. Best Practices

Data preprocessing

We see items, which are obviously not words, such as 00 and 000. Maybe we should ignore items that contain only digits. However, 0d and 0t are also not words. We also see items as __, so maybe we should only allow items that consist only of letters. The posts contain names such as andrew as well. We can filter names with the Names corpus from NLTK we just worked with. Of course, with every filtering we apply, we have to make sure that we don't lose information. Finally, we see words that are very similar, such as try and trying, and word and words.

We have two basic strategies to deal words from the same root--stemming and lemmatization. Stemming is the more quick and dirty type approach. It involves chopping, if necessary, off letters, for example, 'words' becomes 'word' after stemming. The result of stemming doesn't have to be a valid word. Lemmatizing, on the other...

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