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Python Data Analysis

You're reading from   Python Data Analysis Learn how to apply powerful data analysis techniques with popular open source Python modules

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Product type Paperback
Published in Oct 2014
Publisher
ISBN-13 9781783553358
Length 348 pages
Edition 1st Edition
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Author (1):
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Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
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Toc

Table of Contents (17) Chapters Close

Preface 1. Getting Started with Python Libraries FREE CHAPTER 2. NumPy Arrays 3. Statistics and Linear Algebra 4. pandas Primer 5. Retrieving, Processing, and Storing Data 6. Data Visualization 7. Signal Processing and Time Series 8. Working with Databases 9. Analyzing Textual Data and Social Media 10. Predictive Analytics and Machine Learning 11. Environments Outside the Python Ecosystem and Cloud Computing 12. Performance Tuning, Profiling, and Concurrency A. Key Concepts
B. Useful Functions C. Online Resources
Index

Summary

This was a chapter about textual analysis. We learned that it's a best practice in text analysis to get rid of stopwords.

In the bag-of-words model, we created from a document a bag containing words found in the document. Using all the word counts, we can build a feature vector for each document.

Classification algorithms are a type of machine learning algorithm, which involve determining the class of a given item. Naive Bayes classification is a probabilistic algorithm based on the Bayes theorem from probability theory and statistics. The Bayes theorem states that the posterior probability is proportional to the prior probability multiplied by the likelihood.

The next chapter will describe machine learning in more detail. Machine learning is a research field that shows a lot of promise. One day, it may even replace human labor completely. We will explore what we can do with scikit-learn, the Python machine learning package, using weather data as an example.

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