In the previous chapters, we looked at how we can use different algorithms to perform a number of data mining tasks to get the information we are looking for. Now, it's time to put all the knowledge that we have gathered to practical implementation and see how accurately we can predict the results. We will use real-world data and predict the results that will be of importance. We will also frame the problem statement and then apply data mining algorithms to get the prediction for the problem statement.
Mastering SQL Server 2014 Data Mining
By :
Mastering SQL Server 2014 Data Mining
By:
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>
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