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Hands-On Data Science with SQL Server 2017

You're reading from   Hands-On Data Science with SQL Server 2017 Perform end-to-end data analysis to gain efficient data insight

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Product type Paperback
Published in Nov 2018
Publisher Packt
ISBN-13 9781788996341
Length 506 pages
Edition 1st Edition
Languages
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Authors (2):
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Vladimír Mužný Vladimír Mužný
Author Profile Icon Vladimír Mužný
Vladimír Mužný
Marek Chmel Marek Chmel
Author Profile Icon Marek Chmel
Marek Chmel
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Table of Contents (14) Chapters Close

Preface 1. Data Science Overview FREE CHAPTER 2. SQL Server 2017 as a Data Science Platform 3. Data Sources for Analytics 4. Data Transforming and Cleaning with T-SQL 5. Data Exploration and Statistics with T-SQL 6. Custom Aggregations on SQL Server 7. Data Visualization 8. Data Transformations with Other Tools 9. Predictive Model Training and Evaluation 10. Making Predictions 11. Getting It All Together - A Real-World Example 12. Next Steps with Data Science and SQL 13. Other Books You May Enjoy

Deploying, training, and evaluating a predictive model

In this section, we will write code based on a physical data schema that we created in the previous section. In this section, we're going to use R scripting to create a recommender machine learning model. We're not going to explain all aspects of the R language but we will go through the elements that are important to build a fully-functioning machine learning model maintained by SQL Server.

In the previous sections, we configured ML Services on SQL Server and we also imported an external package called recommenderlab. The recommenderlab package, as its name suggests, is used for recommendations. The input for the recommender that is provided by this package is a matrix in the form of items in columns, users in rows, and ratings in the matrix itself.

First, we need to gather some data and create a matrix in this...

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