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Microsoft SQL Server 2014 Business Intelligence Development Beginner's Guide

You're reading from   Microsoft SQL Server 2014 Business Intelligence Development Beginner's Guide Get to grips with Microsoft Business Intelligence and Data Warehousing technologies using this practical guide

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Product type Paperback
Published in May 2014
Publisher
ISBN-13 9781849688888
Length 350 pages
Edition Edition
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Authors (2):
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Reza Rad Reza Rad
Author Profile Icon Reza Rad
Reza Rad
Abolfazl Radgoudarzi Abolfazl Radgoudarzi
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Abolfazl Radgoudarzi
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Table of Contents (19) Chapters Close

Microsoft SQL Server 2014 Business Intelligence Development Beginner's Guide
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Data Warehouse Design 2. SQL Server Analysis Services Multidimensional Cube Development FREE CHAPTER 3. Tabular Model Development of SQL Server Analysis Services 4. ETL with Integration Services 5. Master Data Management 6. Data Quality and Data Cleansing 7. Data Mining – Descriptive Models in SSAS 8. Identifying Data Patterns – Predictive Models in SSAS 9. Reporting Services 10. Dashboard Design 11. Power BI 12. Integrating Reports in Applications Index

Time for action – finding the best mining model with Lift Chart and Profit Chart


In this example, we will add two other data mining algorithms, Naïve Bayes and Clustering, to the Target mail mining Structure example from the previous chapter. Then, we compare Lift Chart and Profit Chart for these algorithms to see which one works better compared to the test set. Perform the following steps to add the algorithms:

  1. Open Target mail mining Structure from the first example of the previous chapter.

  2. In the mining structure designer, go to the Mining Models tab and create a new mining model in one of the following ways:

    • Right-click anywhere on the Mining Models tab and choose New Mining Model

    • Click on the icon that shows the Create a related mining model option on hovering

  3. In the New Mining Model window, choose Microsoft Naïve Bayes as the algorithm and name it Target Mail Naive Bayes, as shown in the following screenshot:

  4. Repeat steps 2 and 3 to create a new mining model with the Microsoft Clustering...

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