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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

Subsetting the columns


For this exercise, we will be using a restricted set of columns from the CSV file. We can either select the specific columns from the dataframe just read in (if we just read in the whole file), or reread the csv file using the colClasses parameter to only read the columns that are required. Often, this method is preferable when you are reading a large file, and will instruct read.csv to only retain the first three and the last two columns, and ignore the columns priemp through govmilitary.

After rereading in the file, with a subset of the columns, we print a few records from the beginning and end of the file. We can do this using a combination of the rbind(), head(), and tail() functions. This will give us all of the columns we will be using for this chapter, except for some columns, which we will derive in the next section:

x <- read.csv("hihist2bedit.csv", colClasses = c(NA,NA, NA, NA, rep("NULL", 7)))

 rbind(head(x), tail(x)) 
>          Year Year.1 Total.People...
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