Mastering SQL Server 2014 Data Mining

Master selecting, applying, and deploying data mining models to build powerful predictive analysis frameworks
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Mastering SQL Server 2014 Data Mining

Amarpreet Singh Bassan, Debarchan Sarkar

Master selecting, applying, and deploying data mining models to build powerful predictive analysis frameworks
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Book Details

ISBN 139781849688949
Paperback304 pages

Book Description

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.

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.

Table of Contents

Chapter 1: Identifying, Staging, and Understanding Data
Data mining life cycle
Staging data
Understanding and cleansing data
Summary
Chapter 2: Data Model Preparation and Deployment
Preparing data models
Validating data models
Deploying data models
Summary
Chapter 3: Tools of the Trade
SQL Server BI Suite
References
Summary
Chapter 4: Preparing the Data
Listing of popular databases
Summary
Chapter 5: Classification Models
Input, output, and predicted columns
The feature selection
The Microsoft Decision Tree algorithm
The Microsoft Neural Network algorithm
The Microsoft Naïve Bayes algorithm
Summary
Chapter 6: Segmentation and Association Models
The Microsoft Clustering algorithm
The Microsoft Association algorithm
Summary
Chapter 7: Sequence and Regression Models
The Microsoft Sequence Clustering algorithm
The Microsoft Time Series algorithm
Summary
Chapter 8: Data Mining Using Excel and Big Data
Data mining using Microsoft Excel
Data mining using HDInsight and Microsoft Azure Machine Learning
Summary
Chapter 9: Tuning the Models
Getting the real-world data
Adding a clustering model to the data mining structure
Adding the Neural Network model to the data mining structure
Summary
Chapter 10: Troubleshooting
A fraction of rows get transferred into a SQL table
Error during changing of the data type of the table
Troubleshooting the data mining structure performance
Error during the deployment of a model
Summary

What You Will Learn

  • Get an overview of the data mining life cycle
  • Understand the intricacies of SQL Server BI Suite with the help of a practical example
  • Collate data from diverse data sources and build a data warehouse
  • Gain in-depth knowledge about the various data mining models such as classification, segmentation, association, and more
  • Perform data mining using Big Data and Excel add-ins
  • Work on real-world data and gain insights into it using various data mining algorithms
  • Fine tune data mining models
  • Troubleshoot problems encountered during data mining activities performed in this book

Authors

Table of Contents

Chapter 1: Identifying, Staging, and Understanding Data
Data mining life cycle
Staging data
Understanding and cleansing data
Summary
Chapter 2: Data Model Preparation and Deployment
Preparing data models
Validating data models
Deploying data models
Summary
Chapter 3: Tools of the Trade
SQL Server BI Suite
References
Summary
Chapter 4: Preparing the Data
Listing of popular databases
Summary
Chapter 5: Classification Models
Input, output, and predicted columns
The feature selection
The Microsoft Decision Tree algorithm
The Microsoft Neural Network algorithm
The Microsoft Naïve Bayes algorithm
Summary
Chapter 6: Segmentation and Association Models
The Microsoft Clustering algorithm
The Microsoft Association algorithm
Summary
Chapter 7: Sequence and Regression Models
The Microsoft Sequence Clustering algorithm
The Microsoft Time Series algorithm
Summary
Chapter 8: Data Mining Using Excel and Big Data
Data mining using Microsoft Excel
Data mining using HDInsight and Microsoft Azure Machine Learning
Summary
Chapter 9: Tuning the Models
Getting the real-world data
Adding a clustering model to the data mining structure
Adding the Neural Network model to the data mining structure
Summary
Chapter 10: Troubleshooting
A fraction of rows get transferred into a SQL table
Error during changing of the data type of the table
Troubleshooting the data mining structure performance
Error during the deployment of a model
Summary

Book Details

ISBN 139781849688949
Paperback304 pages
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