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Apache Spark Machine Learning Blueprints

You're reading from   Apache Spark Machine Learning Blueprints Develop a range of cutting-edge machine learning projects with Apache Spark using this actionable guide

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Product type Paperback
Published in May 2016
Publisher Packt
ISBN-13 9781785880391
Length 252 pages
Edition 1st Edition
Languages
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Author (1):
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Alex Liu Alex Liu
Author Profile Icon Alex Liu
Alex Liu
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Table of Contents (13) Chapters Close

Preface 1. Spark for Machine Learning FREE CHAPTER 2. Data Preparation for Spark ML 3. A Holistic View on Spark 4. Fraud Detection on Spark 5. Risk Scoring on Spark 6. Churn Prediction on Spark 7. Recommendations on Spark 8. Learning Analytics on Spark 9. City Analytics on Spark 10. Learning Telco Data on Spark 11. Modeling Open Data on Spark Index

Data and feature preparation


In the section Feature extraction of Chapter 2, Data Preparation for Spark ML, we have reviewed a few methods for feature extraction, and discussed their implementation in Apache Spark. All the techniques discussed there can be applied to the risk scoring project here.

For this project, as mentioned earlier, the main concern is to get everything organized as workflows for repeatability, and possibly automation. So we will adopt OpenRefine for data and feature preparation. We will use OpenRefine within the DataScientistWorkbench environment where it has been integrated.

OpenRefine

OpenRefine, formerly Google Refine, is an open source application for data cleaning.

To use OpenRefine, please go to: https://datascientistworkbench.com/

After logging in, you will see the following screen:

Then, please click on the OpenRefine button on the upper-right corner of the screen:

Here, you can import datasets from your computer or from a URL address.

Then you can create an OpenRefine...

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