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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

Performing some automated forecasting using the ets function

So far, we have looked at ways in which we can explore any linear trends which may be inherent in our data. That provided a solid foundation for the next step, prediction. Now we will begin to look at how we can perform some actual forecasting.

Converting the dataframe to a time series object

As a preparation step, we will use the ts function to convert our dataframe to a time series object. It is important that the time series be equally spaced before converting to a ts object. At a minimum, you supply the time series variable, and start and end dates as arguments to the ts function.

After creating a new object, x, run a str() function to verify that all of the...

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