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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

Some useful Spark functions to explore your data

Count and groupby

We can also use the Count and groupby functions to aggregate individual variables.

Here is an example of using this to tally the number of observations by outcome. Since the result is another dataframe, we can use the head function to write the results to the console.

You might have to alter the number of rows returned by head if you change the query. It is always a good idea to filter results using a function such as head, to make sure that you are not printing hundreds of rows (or more).

However, you also need to ensure that you do not cut off all of your output. If you are unsure as to the number of rows, first assign the result to a dataframe and then check...

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