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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

Reading the table

Once you have completed the preceding steps, the table you have just created will be registered in the databricks system and will remain persistent across sessions, i.e you will not need to reload the data every time you login.

Running the first cell

Begin by running the first cell (also referred to as a code chunk), which simply will get a count of the number of records by year. You can access the code in this chapter by downloading it from the book's site. Alternatively, you can copy each section of the following code into a new cell and create your own notebook that way.

Since stop frisk has been imported and has already been registered as a table, we can begin to use SQL to read it some of the counts...

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