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Practical Predictive Analytics

You're reading from   Practical Predictive Analytics Analyse current and historical data to predict future trends using R, Spark, and more

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Product type Paperback
Published in Jun 2017
Publisher Packt
ISBN-13 9781785886188
Length 576 pages
Edition 1st Edition
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Author (1):
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Ralph Winters Ralph Winters
Author Profile Icon Ralph Winters
Ralph Winters
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Table of Contents (13) Chapters Close

Preface 1. Getting Started with Predictive Analytics FREE CHAPTER 2. The Modeling Process 3. Inputting and Exploring Data 4. Introduction to Regression Algorithms 5. Introduction to Decision Trees, Clustering, and SVM 6. Using Survival Analysis to Predict and Analyze Customer Churn 7. Using Market Basket Analysis as a Recommender Engine 8. Exploring Health Care Enrollment Data as a Time Series 9. Introduction to Spark Using R 10. Exploring Large Datasets Using Spark 11. Spark Machine Learning - Regression and Cluster Models 12. Spark Models – Rule-Based Learning

Data input


Data by itself is just a pure stream of a numerical something. It is the analytics process that turns this something into knowledge, but before we start understanding it, we have to be able to obtain it. The number of ways in which we can now generate data has grown exponentially. Progressing from fixed length format, through HTML, and then to free form unstructured input, and then to today's schema on read technologies, there are so many different data formats today that there is a very good chance that you haven't, and will never work with a few of them. Reading data in and understanding the variables and what the data represents can also be incredibly frustrating. Integrating the data with other sources, both internal and external, can seem like a jigsaw puzzle at times. At times, the data will not seem to fit as nicely as you would hope.

However, with regard to raw input, most people will work with a couple of common formats in the course of their work, and it will be useful...

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