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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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Toc

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

Creating some global views

Creating global views will also allow us to pass data between different databricks notebooks. These views will be referenced in the next section. Use the %sql magic command as the first line in the databricks notebook to signify that these are SQL statements:

%sql 
CREATE GLOBAL TEMPORARY VIEW df_view AS SELECT * FROM df

%sql
CREATE GLOBAL TEMPORARY VIEW test_view AS SELECT * FROM test

%sql
CREATE GLOBAL TEMPORARY VIEW out_sd_view AS SELECT * FROM out_sd

%sql
CREATE GLOBAL TEMPORARY VIEW sumdf_view AS SELECT * FROM sumdf

User exercise

After the views have been created, use SQL to read back the counts and verify the totals with the row counts produced for the original dataframes:

%sql
select count...
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