Are you a data mining analyst, who spends up to 80% of your time assuring data quality, then preparing that data for developing and deploying predictive models? And do you find lots of literature on data mining theory and concepts, but when it comes to practical advice on developing good mining views find little "how to information? And are you, like most analysts, preparing the data in SAS?This book is intended to fill this gap as your source of practical recipes. It introduces a framework for the process of data preparation for data mining, and presents the detailed implementation of each step in SAS. In addition, business applications of data mining modeling require you to deal with a large number of variables, typically hundreds if not thousands. Therefore, the book devotes several chapters to the methods of data transformation and variable selection. - A complete framework for the data preparation process, including implementation details for each step. - The complete SAS implementation code, which is readily usable by professional analysts and data miners. - A unique and comprehensive approach for the treatment of missing values, optimal binning, and cardinality reduction. - Assumes minimal proficiency in SAS and includes a quick-start chapter on writing SAS macros.

Mamdouh Refaat is a data mining and business analytics consultant advising major organizations in North America and Europe. He has held several positions in consulting organizations and software vendors, including the director of consulting services at ANGOSS Software Corporation, a global data mining software and service provider. During his career, Mamdouh has managed numerous data mining consulting projects in marketing, CRM, and credit risk for Fortune 500 organizations in North America and Europe. In addition, he has delivered over 50 professional training courses in data mining and business analytics. Mamdouh holds a Ph.D. in Engineering from the University of Toronto, and an MBA from the University of Leeds.

During his career, Mamdouh has managed numerous data mining consulting projects in marketing, CRM, and credit risk for Fortune 500 organizations in North America and Europe. In addition, he has delivered over 50 professional training courses in data mining and business analytics.

Mamdouh holds a PhD in Engineering from the University of Toronto, and an MBA from the University of Leeds.

Contents
1 Introduction
2 Tasks and Data Flow
3 Review of Data Mining Modeling Techniques
4 SAS Macros: A Quick Start
5 Data Acquisition and Integration
6 Integrity Checks
8 Sampling and Partitioning
9 Data Transformations
10 Binning and Reduction of Cardinality
11 Treatment of Missing Values
12 Predictive Power and Variable Reduction I
13 Analysis of Nominal and Ordinal Variables
14 Analysis of Continuous Variables
15 Principal Component Analysis (PCA) 2
16 Factor Analysis
17 Predictive Power and Variable Reduction II
18 Putting it All Together
A Listing of SAS Macros