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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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Toc

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

Another OneR example

This example uses the much larger diabetes dataset. Since most of the variables in this dataset are numeric, OneR can bin all of them:

  1. First, read the Spark diabetes table using SQL, which has already been registered in a previous chapter.
  2. Collect a 15% random sample of the data and assign it to an R (not Spark!) dataframe named "local".
  3. Bin all of the available variables based upon their ability to predict the outcome and assign it to an R dataframe named "data":
        library(OneR) 
df = sql("SELECT outcome, age, mass, triceps, pregnant,
glucose, pressure, insulin, pedigree
FROM global_temp.df_view")

local = collect(sample(df, F,.15))

data <- optbin(local,outcome~.)
summary(data)
  1. Run the OneR model using all of the variables to predict the outcome. Recall that the outcome...
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