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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

Creating new columns

Usually it's necessary to create some new transformation based on existing variables which will improve a prediction. We have already seen that binning a variable is often done to create a nominal variable from a quantitative one.

Let's create a new column, called agecat, which divides age into two segments. To keep things simple, we will start off by rounding the age to the nearest integer.

filtered <- SparkR::filter(out_sd, "age > 0 AND insulin > 0") 
filtered$age <- round(filtered$age,0)
filtered$agecat <- ifelse(filtered$age <= 35,"<= 35","35 Or Older")
SparkR::head(SparkR::select(filtered, "age","agecat"))

In the code which you just ran, you may notice that some commands are prefaced by SparkR::

This is done to let the program know which version of the function we wish...

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