In the previous chapter, we looked at the clustering and the association rules algorithms, which basically gave us an idea of how to identify associated groups in our dataset. Let's now consider a problem of a marketing manager of a popular online retailer site. Here, there are various categories of products sold, including books, magazines, electronics, cookware, office products, and so on. Every day, thousands of web customers come to the site, navigating to different domains of the portal of the retail shop. In a physical shop, these departments and products that attract most customers and the customer interactions on various products are easily identifiable. In a virtual store, the manager cannot see the customers. However, it is important to learn more about his customers to provide them with better services. It is important to determine how the customers are using the site and the list of products for which they have shown interest. There is...
Mastering SQL Server 2014 Data Mining
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Mastering SQL Server 2014 Data Mining
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Overview of this book
<p>Whether you are new to data mining or are a seasoned expert, this book will provide you with the skills you need to successfully create, customize, and work with Microsoft Data Mining Suite. Starting with the basics, this book will cover how to clean the data, design the problem, and choose a data mining model that will give you the most accurate prediction.</p>
<p>Next, you will be taken through the various classification models such as the decision tree data model, neural network model, as well as Naïve Bayes model. Following this, you'll learn about the clustering and association algorithms, along with the sequencing and regression algorithms, and understand the data mining expressions associated with each algorithm. With ample screenshots that offer a step-by-step account of how to build a data mining solution, this book will ensure your success with this cutting-edge data mining system.</p>
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Table of Contents (17 chapters)
Mastering SQL Server 2014 Data Mining
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
Identifying, Staging, and Understanding Data
Data Model Preparation and Deployment
Tools of the Trade
Preparing the Data
Classification Models
Segmentation and Association Models
Sequence and Regression Models
Data Mining Using Excel and Big Data
Tuning the Models
Troubleshooting
Index
Customer Reviews