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Mastering Predictive Analytics with Python

You're reading from   Mastering Predictive Analytics with Python Exploit the power of data in your business by building advanced predictive modeling applications with Python

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Product type Paperback
Published in Aug 2016
Publisher
ISBN-13 9781785882715
Length 334 pages
Edition 1st Edition
Languages
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Author (1):
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Joseph Babcock Joseph Babcock
Author Profile Icon Joseph Babcock
Joseph Babcock
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Table of Contents (11) Chapters Close

Preface 1. From Data to Decisions – Getting Started with Analytic Applications FREE CHAPTER 2. Exploratory Data Analysis and Visualization in Python 3. Finding Patterns in the Noise – Clustering and Unsupervised Learning 4. Connecting the Dots with Models – Regression Methods 5. Putting Data in its Place – Classification Methods and Analysis 6. Words and Pixels – Working with Unstructured Data 7. Learning from the Bottom Up – Deep Networks and Unsupervised Features 8. Sharing Models with Prediction Services 9. Reporting and Testing – Iterating on Analytic Systems Index

Scaling out with PySpark – predicting year of song release

To close, let us look at another example using PySpark. With this dataset, which is a subset of the Million Song dataset (Bertin-Mahieux, Thierry, et al. "The million song dataset." ISMIR 2011: Proceedings of the 12th International Society for Music Information Retrieval Conference, October 24-28, 2011, Miami, Florida. University of Miami, 2011), the goal is to predict the year of a song's release based on the features of the track. The data is supplied as a comma-separated text file, which we can convert into an RDD using the Spark textFile() function. As before in our clustering example, we also define a parsing function with a try…catch block so that we do not fail on a single error in a large dataset:

>>> def parse_line(l):
…      try:
…            return l.split(",")
…    except:
…         print("error in processing {0}".format(l))

We then...

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