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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 2. The Modeling Process FREE CHAPTER 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

Inputting and Exploring Data

"On two occasions I have been asked, "Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?" ... I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question."
-Charles Babbage

In this chapter, we will cover inputting and exploring data. In the first two chapters, we covered processing some datasets that already reside within R packages. We purposefully avoided reading any external data sources. However, now we will. The inputting data section will cover various mechanisms for reading your own data into R.

The exploring data section covers some techniques you can use to implement successful completion of the second and third of the data understanding and data preparation steps of the CRISP-DM process we covered in the last chapter.

The topics we will...

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