Data Preparation For Data Mining Using Sas

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Data Preparation for Data Mining Using SAS

Mamdouh Refaat
Data Preparation for Data Mining Using SAS Book PDF

Author : Mamdouh Refaat
Publisher : Elsevier
Page : 424 pages
File Size : 47,6 Mb
Release : 2010-07-27
Category : Computers
ISBN : 0080491006

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Data Preparation for Data Mining Using SAS by Mamdouh Refaat Pdf

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.

Data Preparation for Data Mining Using SAS

Mamdouh Refaat
Data Preparation for Data Mining Using SAS Book PDF

Author : Mamdouh Refaat
Publisher : Unknown
Page : 0 pages
File Size : 54,5 Mb
Release : 2010
Category : Electronic
ISBN : OCLC:1371785127

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Data Preparation for Data Mining Using SAS by Mamdouh Refaat Pdf

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 toň 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.

Data Preparation for Analytics Using SAS

Gerhard Svolba
Data Preparation for Analytics Using SAS Book PDF

Author : Gerhard Svolba
Publisher : SAS Institute
Page : 440 pages
File Size : 46,9 Mb
Release : 2006-11-01
Category : Computers
ISBN : 9781599943367

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Data Preparation for Analytics Using SAS by Gerhard Svolba Pdf

Text addresses such tasks as: viewing analytic data preparation in the context of its business environment, identifying the specifics of predictive modeling for data mart creation, understanding the concepts and considerations of data preparation for time series analysis, and using SAS procedures for scoring.

Data Mining Using SAS Enterprise Miner

Randall Matignon
Data Mining Using SAS Enterprise Miner Book PDF

Author : Randall Matignon
Publisher : John Wiley & Sons
Page : 584 pages
File Size : 55,5 Mb
Release : 2007-08-03
Category : Mathematics
ISBN : 9780470149010

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Data Mining Using SAS Enterprise Miner by Randall Matignon Pdf

The most thorough and up-to-date introduction to data mining techniques using SAS Enterprise Miner. The Sample, Explore, Modify, Model, and Assess (SEMMA) methodology of SAS Enterprise Miner is an extremely valuable analytical tool for making critical business and marketing decisions. Until now, there has been no single, authoritative book that explores every node relationship and pattern that is a part of the Enterprise Miner software with regard to SEMMA design and data mining analysis. Data Mining Using SAS Enterprise Miner introduces readers to a wide variety of data mining techniques and explains the purpose of-and reasoning behind-every node that is a part of the Enterprise Miner software. Each chapter begins with a short introduction to the assortment of statistics that is generated from the various nodes in SAS Enterprise Miner v4.3, followed by detailed explanations of configuration settings that are located within each node. Features of the book include: The exploration of node relationships and patterns using data from an assortment of computations, charts, and graphs commonly used in SAS procedures A step-by-step approach to each node discussion, along with an assortment of illustrations that acquaint the reader with the SAS Enterprise Miner working environment Descriptive detail of the powerful Score node and associated SAS code, which showcases the important of managing, editing, executing, and creating custom-designed Score code for the benefit of fair and comprehensive business decision-making Complete coverage of the wide variety of statistical techniques that can be performed using the SEMMA nodes An accompanying Web site that provides downloadable Score code, training code, and data sets for further implementation, manipulation, and interpretation as well as SAS/IML software programming code This book is a well-crafted study guide on the various methods employed to randomly sample, partition, graph, transform, filter, impute, replace, cluster, and process data as well as interactively group and iteratively process data while performing a wide variety of modeling techniques within the process flow of the SAS Enterprise Miner software. Data Mining Using SAS Enterprise Miner is suitable as a supplemental text for advanced undergraduate and graduate students of statistics and computer science and is also an invaluable, all-encompassing guide to data mining for novice statisticians and experts alike.

Statistical Data Mining Using SAS Applications

George Fernandez
Statistical Data Mining Using SAS Applications Book PDF

Author : George Fernandez
Publisher : CRC Press
Page : 477 pages
File Size : 55,7 Mb
Release : 2010-06-18
Category : Business & Economics
ISBN : 9781439810767

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Statistical Data Mining Using SAS Applications by George Fernandez Pdf

Statistical Data Mining Using SAS Applications, Second Edition describes statistical data mining concepts and demonstrates the features of user-friendly data mining SAS tools. Integrating the statistical and graphical analysis tools available in SAS systems, the book provides complete statistical data mining solutions without writing SAS program co

Applied Data Mining for Forecasting Using SAS(R)

Tim Rey ,Arthur Kordon,Chip Wells
Applied Data Mining for Forecasting Using SAS R Book PDF

Author : Tim Rey ,Arthur Kordon,Chip Wells
Publisher : SAS Institute
Page : 336 pages
File Size : 46,7 Mb
Release : 2012-07-02
Category : Computers
ISBN : 9781612900933

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Applied Data Mining for Forecasting Using SAS(R) by Tim Rey ,Arthur Kordon,Chip Wells Pdf

Applied Data Mining for Forecasting Using SAS, by Tim Rey, Arthur Kordon, and Chip Wells, introduces and describes approaches for mining large time series data sets. Written for forecasting practitioners, engineers, statisticians, and economists, the book details how to select useful candidate input variables for time series regression models in environments when the number of candidates is large, and identifies the correlation structure between selected candidate inputs and the forecast variable. This book is essential for forecasting practitioners who need to understand the practical issues involved in applied forecasting in a business setting. Through numerous real-world examples, the authors demonstrate how to effectively use SAS software to meet their industrial forecasting needs. This book is part of the SAS Press program.

Data Preparation for Data Mining

Dorian Pyle
Data Preparation for Data Mining Book PDF

Author : Dorian Pyle
Publisher : Morgan Kaufmann
Page : 566 pages
File Size : 43,6 Mb
Release : 1999-03-22
Category : Computers
ISBN : 1558605290

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Data Preparation for Data Mining by Dorian Pyle Pdf

This book focuses on the importance of clean, well-structured data as the first step to successful data mining. It shows how data should be prepared prior to mining in order to maximize mining performance.

First Steps in Data Mining with SAS Enterprise Miner

Martha Abell
First Steps in Data Mining with SAS Enterprise Miner Book PDF

Author : Martha Abell
Publisher : CreateSpace
Page : 72 pages
File Size : 46,6 Mb
Release : 2014-09-06
Category : Electronic
ISBN : 1501078933

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First Steps in Data Mining with SAS Enterprise Miner by Martha Abell Pdf

SAS Enterprise Miner streamlines the data mining process to create highly accurate predictive and descriptive models based on analysis of vast amounts of data from across an enterprise. Data mining is applicable in a variety of industries and provides methodologies for such diverse business problems as fraud detection, householding, customer retention and attrition, database marketing, market segmentation, risk analysis, affinity analysis, customer satisfaction, bankruptcy prediction, and portfolio analysis. In SAS Enterprise Miner, the data mining process has the following (SEMMA) steps: Sample the data by creating one or more data sets. The sample should be large enough to contain significant information, yet small enough to process. This step includes the use of data preparation tools for data import, merge, append, and filter, as well as statistical sampling techniques. Explore the data by searching for relationships, trends, and anomalies in order to gain understanding and ideas. This step includes the use of tools for statistical reporting and graphical exploration, variable selection methods, and variable clustering. Modify the data by creating, selecting, and transforming the variables to focus the model selection process. This step includes the use of tools for defining transformations, missing value handling, value recoding, and interactive binning. Model the data by using the analytical tools to train a statistical or machine learning model to reliably predict a desired outcome. This step includes the use of techniques such as linear and logistic regression, decision trees, neural networks, partial least squares, LARS and LASSO, nearest neighbor, and importing models defined by other users or even outside SAS Enterprise Miner. Assess the data by evaluating the usefulness and reliability of the findings from the data mining process. This step includes the use of tools for comparing models and computing new fit statistics, cutoff analysis, decision support, report generation, and score code management. You might or might not include all of the SEMMA steps in an analysis, and it might be necessary to repeat one or more of the steps several times before you are satisfied with the results. After you have completed the SEMMA steps, you can apply a scoring formula from one or more champion models to new data that might or might not contain the target variable. Scoring new data that is not available at the time of model training is the goal of most data mining problems. Furthermore, advanced visualization tools enable you to quickly and easily examine large amounts of data in multidimensional histograms and to graphically compare modeling results.

Business Analytics Using SAS Enterprise Guide and SAS Enterprise Miner

Olivia Parr-Rud
Business Analytics Using SAS Enterprise Guide and SAS Enterprise Miner Book PDF

Author : Olivia Parr-Rud
Publisher : SAS Institute
Page : 182 pages
File Size : 54,6 Mb
Release : 2014-10
Category : Computers
ISBN : 9781629593272

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Business Analytics Using SAS Enterprise Guide and SAS Enterprise Miner by Olivia Parr-Rud Pdf

This tutorial for data analysts new to SAS Enterprise Guide and SAS Enterprise Miner provides valuable experience using powerful statistical software to complete the kinds of business analytics common to most industries. This beginnner's guide with clear, illustrated, step-by-step instructions will lead you through examples based on business case studies. You will formulate the business objective, manage the data, and perform analyses that you can use to optimize marketing, risk, and customer relationship management, as well as business processes and human resources. Topics include descriptive analysis, predictive modeling and analytics, customer segmentation, market analysis, share-of-wallet analysis, penetration analysis, and business intelligence. --

Exploring SAS Viya

Sas Education
Exploring SAS Viya Book PDF

Author : Sas Education
Publisher : Unknown
Page : 126 pages
File Size : 55,5 Mb
Release : 2020-01-10
Category : Computers
ISBN : 1642955884

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Exploring SAS Viya by Sas Education Pdf

SAS Visual Data Mining and Machine Learning, powered by SAS Viya, means that users of all skill levels can visually explore data on their own while drawing on powerful in-memory technologies for faster analytic computations and discoveries. You can manually program with custom code or use the features in SAS Studio, Model Studio, and SAS Visual Analytics to automate your data manipulation and modeling. These programs offer a flexible, easy-to-use, self-service environment that can scale on an enterprise-wide level. In this book, we will explore some of the many features of SAS Visual Data Mining and Machine Learning including: programming in the Python interface; new, advanced data mining and machine learning procedures; pipeline building in Model Studio, and model building and comparison in SAS Visual Analytics.

Introduction to Data Mining Using SAS Enterprise Miner

Patricia B. Cerrito
Introduction to Data Mining Using SAS Enterprise Miner Book PDF

Author : Patricia B. Cerrito
Publisher : SAS Press
Page : 0 pages
File Size : 41,7 Mb
Release : 2006
Category : Data mining
ISBN : 1590478290

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Introduction to Data Mining Using SAS Enterprise Miner by Patricia B. Cerrito Pdf

"This manual provides a general, practical introduction to data mining using SAS Enterprise Miner and SAS Text Miner software"--Preface.

Data Mining Using SAS Applications

George Fernandez
Data Mining Using SAS Applications Book PDF

Author : George Fernandez
Publisher : CRC Press
Page : 536 pages
File Size : 54,9 Mb
Release : 2010-12-12
Category : Business & Economics
ISBN : 1420057332

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Data Mining Using SAS Applications by George Fernandez Pdf

Most books on data mining focus on principles and furnish few instructions on how to carry out a data mining project. Data Mining Using SAS Applications not only introduces the key concepts but also enables readers to understand and successfully apply data mining methods using powerful yet user-friendly SAS macro-call files. These methods stress the use of visualization to thoroughly study the structure of data and check the validity of statistical models fitted to data. Learn how to convert PC databases to SAS data Discover sampling techniques to create training and validation samples Understand frequency data analysis for categorical data Explore supervised and unsupervised learning Master exploratory graphical techniques Acquire model validation techniques in regression and classification The text furnishes 13 easy-to-use SAS data mining macros designed to work with the standard SAS modules. No additional modules or previous experience in SAS programming is required. The author shows how to perform complete predictive modeling, including data exploration, model fitting, assumption checks, validation, and scoring new data, on SAS datasets in less than ten minutes!

Machine Learning with SAS Viya

SAS Institute Inc.
Machine Learning with SAS Viya Book PDF

Author : SAS Institute Inc.
Publisher : SAS Institute
Page : 295 pages
File Size : 50,9 Mb
Release : 2020-05-29
Category : Computers
ISBN : 9781951685379

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Machine Learning with SAS Viya by SAS Institute Inc. Pdf

Master machine learning with SAS Viya! Machine learning can feel intimidating for new practitioners. Machine Learning with SAS Viya provides everything you need to know to get started with machine learning in SAS Viya, including decision trees, neural networks, and support vector machines. The analytics life cycle is covered from data preparation and discovery to deployment. Working with open-source code? Machine Learning with SAS Viya has you covered – step-by-step instructions are given on how to use SAS Model Manager tools with open source. SAS Model Studio features are highlighted to show how to carry out machine learning in SAS Viya. Demonstrations, practice tasks, and quizzes are included to help sharpen your skills. In this book, you will learn about: Supervised and unsupervised machine learning Data preparation and dealing with missing and unstructured data Model building and selection Improving and optimizing models Model deployment and monitoring performance

Unstructured Data Analysis

Matthew Windham
Unstructured Data Analysis Book PDF

Author : Matthew Windham
Publisher : SAS Institute
Page : 166 pages
File Size : 49,5 Mb
Release : 2018-09-14
Category : Computers
ISBN : 9781635267099

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Unstructured Data Analysis by Matthew Windham Pdf

Unstructured data is the most voluminous form of data in the world, and several elements are critical for any advanced analytics practitioner leveraging SAS software to effectively address the challenge of deriving value from that data. This book covers the five critical elements of entity extraction, unstructured data, entity resolution, entity network mapping and analysis, and entity management. By following examples of how to apply processing to unstructured data, readers will derive tremendous long-term value from this book as they enhance the value they realize from SAS products.

Handbook of Statistical Analysis and Data Mining Applications

Robert Nisbet,Gary Miner,Ken Yale
Handbook of Statistical Analysis and Data Mining Applications Book PDF

Author : Robert Nisbet,Gary Miner,Ken Yale
Publisher : Elsevier
Page : 822 pages
File Size : 55,7 Mb
Release : 2017-11-09
Category : Mathematics
ISBN : 9780124166455

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Handbook of Statistical Analysis and Data Mining Applications by Robert Nisbet,Gary Miner,Ken Yale Pdf

Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas—from science and engineering, to medicine, academia and commerce. Includes input by practitioners for practitioners Includes tutorials in numerous fields of study that provide step-by-step instruction on how to use supplied tools to build models Contains practical advice from successful real-world implementations Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data mining to build successful data mining solutions Features clear, intuitive explanations of novel analytical tools and techniques, and their practical applications