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SQL Server 2017 Machine Learning Services with R

You're reading from   SQL Server 2017 Machine Learning Services with R Data exploration, modeling, and advanced analytics

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Product type Paperback
Published in Feb 2018
Publisher Packt
ISBN-13 9781787283572
Length 338 pages
Edition 1st Edition
Languages
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Authors (2):
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Julie Koesmarno Julie Koesmarno
Author Profile Icon Julie Koesmarno
Julie Koesmarno
Toma≈æ Ka≈°trun Kaštrun Toma≈æ Ka≈°trun Kaštrun
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Toma≈æ Ka≈°trun Kaštrun
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Toc

Table of Contents (12) Chapters Close

Preface 1. Introduction to R and SQL Server FREE CHAPTER 2. Overview of Microsoft Machine Learning Server and SQL Server 3. Managing Machine Learning Services for SQL Server 2017 and R 4. Data Exploration and Data Visualization 5. RevoScaleR Package 6. Predictive Modeling 7. Operationalizing R Code 8. Deploying, Managing, and Monitoring Database Solutions containing R Code 9. Machine Learning Services with R for DBAs 10. R and SQL Server 2016/2017 Features Extended 11. Other Books You May Enjoy

Built-in JSON capabilities

In this scenario, we will use the EMS incidents by month from the official city of Austin open data portal (https://data.austintexas.gov/Public-Safety/EMS-Incidents-by-Month/gjtj-jt2d). The data essentially contains incident counts, broken down by location and priorities for the city of Austin and Travis County incidents, and the percentage of on-time compliance.

The following are the prerequisites to get started:

  1. Download the data from https://data.austintexas.gov/resource/bpws-iwvb.json to a local path, such as C:\Temp\bpws-iwvb.json.
  2. Grant read access to the directory; for example:
Figure 10.1 Granting access to C:\Temp for MS SQL Server
  1. For ease of R visualization, we will use SQL Operations Studio. You can download SQL Ops Studio from: https://docs.microsoft.com/en-us/sql/sql-operations-studio/download.

The following is an excerpt of a JSON...

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