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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

Becoming large by starting small

The strategy we will use in this chapter is to first retrieve a small existing publicly available dataset (Pima Indians diabetes). Then we will perform some basic exploratory analysis, compute some key statistical properties, and then use those properties to simulate a much larger dataset that we will use to input into Spark. The key characteristics that we will use to generate this 'big data' will be:

  • The means/standard deviations of the variables: the goal will be to generate means and standard deviations for the large dataset, which are close to the equivalent means and standard deviations of the small dataset.
  • The correlations of the variables: since statistical modeling and analysis is largely based upon the association among the variables, the goal of the simulation will be to preserve all of the 2-way correlation numbers for the...
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