IBM SPSS Modeler Cookbook


Author: Keith McCormick,Dean Abbott,Meta S. Brown
Publisher: Packt Pub Limited
ISBN: 9781849685467
Category: Computers
Page: 382
View: 4336
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This is a practical cookbook with intermediate-advanced recipes for SPSS Modeler data analysts. It is loaded with step-by-step examples explaining the process followed by the experts.If you have had some hands-on experience with IBM SPSS Modeler and now want to go deeper and take more control over your data mining process, this is the guide for you. It is ideal for practitioners who want to break into advanced analytics.

IBM SPSS Modeler Cookbook


Author: Keith McCormick,Dean Abbott,Meta S. Brown,Tom Khabaza,Scott R. Mutchler
Publisher: Packt Publishing Ltd
ISBN: 1849685479
Category: Computers
Page: 382
View: 3834
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This is a practical cookbook with intermediate-advanced recipes for SPSS Modeler data analysts. It is loaded with step-by-step examples explaining the process followed by the experts.If you have had some hands-on experience with IBM SPSS Modeler and now want to go deeper and take more control over your data mining process, this is the guide for you. It is ideal for practitioners who want to break into advanced analytics.

Data Mining with SPSS Modeler

Theory, Exercises and Solutions
Author: Tilo Wendler,Sören Gröttrup
Publisher: Springer
ISBN: 3319287095
Category: Mathematics
Page: 1059
View: 9614
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Introducing the IBM SPSS Modeler, this book guides readers through data mining processes and presents relevant statistical methods. There is a special focus on step-by-step tutorials and well-documented examples that help demystify complex mathematical algorithms and computer programs. The variety of exercises and solutions as well as an accompanying website with data sets and SPSS Modeler streams are particularly valuable. While intended for students, the simplicity of the Modeler makes the book useful for anyone wishing to learn about basic and more advanced data mining, and put this knowledge into practice.

IBM SPSS Modeler Essentials

Effective techniques for building powerful data mining and predictive analytics solutions
Author: Jesus Salcedo,Keith McCormick
Publisher: Packt Publishing Ltd
ISBN: 1788296826
Category: Computers
Page: 238
View: 8405
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Get to grips with the fundamentals of data mining and predictive analytics with IBM SPSS Modeler About This Book Get up–and-running with IBM SPSS Modeler without going into too much depth. Identify interesting relationships within your data and build effective data mining and predictive analytics solutions A quick, easy–to-follow guide to give you a fundamental understanding of SPSS Modeler, written by the best in the business Who This Book Is For This book is ideal for those who are new to SPSS Modeler and want to start using it as quickly as possible, without going into too much detail. An understanding of basic data mining concepts will be helpful, to get the best out of the book. What You Will Learn Understand the basics of data mining and familiarize yourself with Modeler's visual programming interface Import data into Modeler and learn how to properly declare metadata Obtain summary statistics and audit the quality of your data Prepare data for modeling by selecting and sorting cases, identifying and removing duplicates, combining data files, and modifying and creating fields Assess simple relationships using various statistical and graphing techniques Get an overview of the different types of models available in Modeler Build a decision tree model and assess its results Score new data and export predictions In Detail IBM SPSS Modeler allows users to quickly and efficiently use predictive analytics and gain insights from your data. With almost 25 years of history, Modeler is the most established and comprehensive Data Mining workbench available. Since it is popular in corporate settings, widely available in university settings, and highly compatible with all the latest technologies, it is the perfect way to start your Data Science and Machine Learning journey. This book takes a detailed, step-by-step approach to introducing data mining using the de facto standard process, CRISP-DM, and Modeler's easy to learn “visual programming” style. You will learn how to read data into Modeler, assess data quality, prepare your data for modeling, find interesting patterns and relationships within your data, and export your predictions. Using a single case study throughout, this intentionally short and focused book sticks to the essentials. The authors have drawn upon their decades of teaching thousands of new users, to choose those aspects of Modeler that you should learn first, so that you get off to a good start using proven best practices. This book provides an overview of various popular data modeling techniques and presents a detailed case study of how to use CHAID, a decision tree model. Assessing a model's performance is as important as building it; this book will also show you how to do that. Finally, you will see how you can score new data and export your predictions. By the end of this book, you will have a firm understanding of the basics of data mining and how to effectively use Modeler to build predictive models. Style and approach This book empowers users to build practical & accurate predictive models quickly and intuitively. With the support of the advanced analytics users can discover hidden patterns and trends.This will help users to understand the factors that influence them, enabling you to take advantage of business opportunities and mitigate risks.

SPSS For Dummies


Author: Arthur Griffith
Publisher: John Wiley & Sons
ISBN: 9780470599990
Category: Mathematics
Page: 384
View: 8726
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Handbook of Statistical Analysis and Data Mining Applications


Author: Robert Nisbet,Gary Miner,Ken Yale
Publisher: Elsevier
ISBN: 0124166458
Category: Mathematics
Page: 822
View: 6933
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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

Drupal 7 Themes


Author: Ric Shreves
Publisher: Packt Publishing Ltd
ISBN: 1849512779
Category: Computers
Page: 299
View: 9217
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Create new themes for your Drupal 7 site with a clean layout and powerful CSS styling.

Play Framework Cookbook


Author: Alexander Reelsen
Publisher: Packt Publishing Ltd
ISBN: 1849515530
Category: Computers
Page: 292
View: 3667
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Over 60 incredibly effective recipes to take you under the hood and leverage advanced concepts of the Play framework.

Applied Predictive Analytics

Principles and Techniques for the Professional Data Analyst
Author: Dean Abbott
Publisher: John Wiley & Sons
ISBN: 1118727967
Category: Computers
Page: 456
View: 8662
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Learn the art and science of predictive analytics — techniques that get results Predictive analytics is what translates big data into meaningful, usable business information. Written by a leading expert in the field, this guide examines the science of the underlying algorithms as well as the principles and best practices that govern the art of predictive analytics. It clearly explains the theory behind predictive analytics, teaches the methods, principles, and techniques for conducting predictive analytics projects, and offers tips and tricks that are essential for successful predictive modeling. Hands-on examples and case studies are included. The ability to successfully apply predictive analytics enables businesses to effectively interpret big data; essential for competition today This guide teaches not only the principles of predictive analytics, but also how to apply them to achieve real, pragmatic solutions Explains methods, principles, and techniques for conducting predictive analytics projects from start to finish Illustrates each technique with hands-on examples and includes as series of in-depth case studies that apply predictive analytics to common business scenarios A companion website provides all the data sets used to generate the examples as well as a free trial version of software Applied Predictive Analytics arms data and business analysts and business managers with the tools they need to interpret and capitalize on big data.

Data Mining For Dummies


Author: Meta S. Brown
Publisher: John Wiley & Sons
ISBN: 1118893174
Category: Computers
Page: 408
View: 9973
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Offers information on how to search through large amounts of computerized business data to find useful patterns or trends, including creation and validity testing of a data model, effective communication of findings, and available tools.

Data Mining and Statistics for Decision Making


Author: Stéphane Tufféry
Publisher: John Wiley & Sons
ISBN: 9780470979280
Category: Computers
Page: 716
View: 5628
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Data mining is the process of automatically searching large volumes of data for models and patterns using computational techniques from statistics, machine learning and information theory; it is the ideal tool for such an extraction of knowledge. Data mining is usually associated with a business or an organization's need to identify trends and profiles, allowing, for example, retailers to discover patterns on which to base marketing objectives. This book looks at both classical and recent techniques of data mining, such as clustering, discriminant analysis, logistic regression, generalized linear models, regularized regression, PLS regression, decision trees, neural networks, support vector machines, Vapnik theory, naive Bayesian classifier, ensemble learning and detection of association rules. They are discussed along with illustrative examples throughout the book to explain the theory of these methods, as well as their strengths and limitations. Key Features: Presents a comprehensive introduction to all techniques used in data mining and statistical learning, from classical to latest techniques. Starts from basic principles up to advanced concepts. Includes many step-by-step examples with the main software (R, SAS, IBM SPSS) as well as a thorough discussion and comparison of those software. Gives practical tips for data mining implementation to solve real world problems. Looks at a range of tools and applications, such as association rules, web mining and text mining, with a special focus on credit scoring. Supported by an accompanying website hosting datasets and user analysis. Statisticians and business intelligence analysts, students as well as computer science, biology, marketing and financial risk professionals in both commercial and government organizations across all business and industry sectors will benefit from this book.

SPSS Statistics for Data Analysis and Visualization


Author: Keith McCormick,Jesus Salcedo,Jon Peck,Andrew Wheeler
Publisher: John Wiley & Sons
ISBN: 1119003555
Category: Computers
Page: 528
View: 9221
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Dive deeper into SPSS Statistics for more efficient, accurate, and sophisticated data analysis and visualization SPSS Statistics for Data Analysis and Visualization goes beyond the basics of SPSS Statistics to show you advanced techniques that exploit the full capabilities of SPSS. The authors explain when and why to use each technique, and then walk you through the execution with a pragmatic, nuts and bolts example. Coverage includes extensive, in-depth discussion of advanced statistical techniques, data visualization, predictive analytics, and SPSS programming, including automation and integration with other languages like R and Python. You'll learn the best methods to power through an analysis, with more efficient, elegant, and accurate code. IBM SPSS Statistics is complex: true mastery requires a deep understanding of statistical theory, the user interface, and programming. Most users don't encounter all of the methods SPSS offers, leaving many little-known modules undiscovered. This book walks you through tools you may have never noticed, and shows you how they can be used to streamline your workflow and enable you to produce more accurate results. Conduct a more efficient and accurate analysis Display complex relationships and create better visualizations Model complex interactions and master predictive analytics Integrate R and Python with SPSS Statistics for more efficient, more powerful code These "hidden tools" can help you produce charts that simply wouldn't be possible any other way, and the support for other programming languages gives you better options for solving complex problems. If you're ready to take advantage of everything this powerful software package has to offer, SPSS Statistics for Data Analysis and Visualization is the expert-led training you need.

Decision Trees and Applications with IBM SPSS Modeler


Author: Marvin L.
Publisher: N.A
ISBN: 9781540754837
Category:
Page: 180
View: 3321
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A wide range of applications, such as R, SAS, MATLAB, and SPSS Statistics, provide a huge toolbox of methods to analyze large data and can be used by experts to find patterns and interesting structures in the data. Many of these tools are mainly programming languages, which assumes the analyst has deeper programming skills and an advanced background in IT and mathematics. Since this field is becoming more important, graphic user-interfaced data analysis software is starting to enter the market, providing "drag and drop" mechanisms for career changers and people who are not experts in programming or statistics.One of these easy to handle, data analytics applications is the IBM SPSS Modeler. This book is dedicated to the introduction and explanation of its data analysis power and focused in decision trees. The more important topics are the next: Decision Tree Models General Uses of Tree-Based Analysis C&RT Algorithms CHAID Algorithms QUEST Algorithms C5.0 Algorithms Decision Trees with IM SPSS Modeler Building a Decision Tree with the C5.0 Node Building a decision tree with the CHAID node The C&R Tree node and variable generation The QUEST node-Boosting & Imbalanced data Detection of diabetes-comparison of decision tree nodes Rule set and cross-validation with C5.0 The Auto Classifier Node Building a Stream with the Auto Classifier Node The Auto Classifier Model Nugget Models for credit rating with the Auto Classifier node SVM classifier Interactive decision Trees with IBM SPSS Modeler The Interactive Tree Builder Growing and Pruning the Tree Defining Custom Splits Customizing the Tree View Gains Risks The Growing Directives Generation Filter and Select Nodes Building a Tree Model Directly C&R Tree, CHAID, QUEST, and C 5.0 Models Nuggets Model Nuggets for Boosting, Bagging and Very Large Datasets

Effective CRM Using Predictive Analytics


Author: Antonios Chorianopoulos
Publisher: John Wiley & Sons
ISBN: 1119011558
Category: BUSINESS & ECONOMICS
Page: 392
View: 8092
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A step-by-step guide to data mining applications in CRM. Following a handbook approach, this book bridges the gap between analytics and their use in everyday marketing, providing guidance on solving real business problems using data mining techniques. The book is organized into three parts. Part one provides a methodological roadmap, covering both the business and the technical aspects. The data mining process is presented in detail along with specific guidelines for the development of optimized acquisition, cross/ deep/ up selling and retention campaigns, as well as effective customer segmentation schemes. Additionally, some of the most useful data mining algorithms are explained in a simple and comprehensive way for business users with no technical expertise. In part two, some of the most useful data mining algorithms are explained in a simple and comprehensive way for business users with no technical expertise. Part three is packed with real world case studies which employ the use of three leading data mining tools: IBM SPSS Modeler, RapidMiner and Data Mining for Excel. Case studies from industries including banking, retail and telecommunications are presented in detail so as to serve as templates for developing similar applications. Key Features: Includes numerous real-world case studies which are presented step by step, demystifying the usage of data mining models and clarifying all the methodological issues. Topics are presented with the use of three leading data mining tools: IBM SPSS Modeler, RapidMiner and Data Mining for Excel. Accompanied by a website featuring material from each case study, including datasets and relevant code. Combining data mining and business knowledge, this practical book provides all the necessary information for designing, setting up, executing and deploying data mining techniques in CRM. Effective CRM using Predictive Analytics will benefit data mining practitioners and consultants, data analysts, statisticians, and CRM officers. The book will also be useful to academics and students interested in applied data mining.

Data Mining Techniques in CRM

Inside Customer Segmentation
Author: Konstantinos K. Tsiptsis,Antonios Chorianopoulos
Publisher: John Wiley & Sons
ISBN: 1119965454
Category: Computers
Page: 372
View: 7179
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This is an applied handbook for the application of data mining techniques in the CRM framework. It combines a technical and a business perspective to cover the needs of business users who are looking for a practical guide on data mining. It focuses on Customer Segmentation and presents guidelines for the development of actionable segmentation schemes. By using non-technical language it guides readers through all the phases of the data mining process.

Nagios Core Administration Cookbook


Author: Tom Ryder
Publisher: Packt Publishing Ltd
ISBN: 1849515573
Category: Computers
Page: 366
View: 9972
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This book is written in Cookbook style, beginning with recipes based on basic structure which gradually progresses towards using Nagios Core as a monitoring framework. This book is for System Administrators who are looking for recipes to help them deal with advanced network monitoring issues with Nagios Core.

Commercial Data Mining

Processing, Analysis and Modeling for Predictive Analytics Projects
Author: David Nettleton
Publisher: Elsevier
ISBN: 012416658X
Category: Computers
Page: 304
View: 4052
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Whether you are brand new to data mining or working on your tenth predictive analytics project, Commercial Data Mining will be there for you as an accessible reference outlining the entire process and related themes. In this book, you'll learn that your organization does not need a huge volume of data or a Fortune 500 budget to generate business using existing information assets. Expert author David Nettleton guides you through the process from beginning to end and covers everything from business objectives to data sources, and selection to analysis and predictive modeling. Commercial Data Mining includes case studies and practical examples from Nettleton's more than 20 years of commercial experience. Real-world cases covering customer loyalty, cross-selling, and audience prediction in industries including insurance, banking, and media illustrate the concepts and techniques explained throughout the book. Illustrates cost-benefit evaluation of potential projects Includes vendor-agnostic advice on what to look for in off-the-shelf solutions as well as tips on building your own data mining tools Approachable reference can be read from cover to cover by readers of all experience levels Includes practical examples and case studies as well as actionable business insights from author's own experience

Practical Data Science

A Guide to Building the Technology Stack for Turning Data Lakes into Business Assets
Author: Andreas François Vermeulen
Publisher: Apress
ISBN: 148423054X
Category: Computers
Page: 805
View: 9554
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Learn how to build a data science technology stack and perform good data science with repeatable methods. You will learn how to turn data lakes into business assets. The data science technology stack demonstrated in Practical Data Science is built from components in general use in the industry. Data scientist Andreas Vermeulen demonstrates in detail how to build and provision a technology stack to yield repeatable results. He shows you how to apply practical methods to extract actionable business knowledge from data lakes consisting of data from a polyglot of data types and dimensions. What You'll Learn Become fluent in the essential concepts and terminology of data science and data engineering Build and use a technology stack that meets industry criteria Master the methods for retrieving actionable business knowledge Coordinate the handling of polyglot data types in a data lake for repeatable results Who This Book Is For Data scientists and data engineers who are required to convert data from a data lake into actionable knowledge for their business, and students who aspire to be data scientists and data engineers

Real-World Data Mining

Applied Business Analytics and Decision Making
Author: Dursun Delen
Publisher: FT Press
ISBN: 0133551113
Category: Computers
Page: 288
View: 3958
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Use the latest data mining best practices to enable timely, actionable, evidence-based decision making throughout your organization! Real-World Data Mining demystifies current best practices, showing how to use data mining to uncover hidden patterns and correlations, and leverage these to improve all aspects of business performance. Drawing on extensive experience as a researcher, practitioner, and instructor, Dr. Dursun Delen delivers an optimal balance of concepts, techniques and applications. Without compromising either simplicity or clarity, he provides enough technical depth to help readers truly understand how data mining technologies work. Coverage includes: processes, methods, techniques, tools, and metrics; the role and management of data; text and web mining; sentiment analysis; and Big Data integration. Throughout, Delen's conceptual coverage is complemented with application case studies (examples of both successes and failures), as well as simple, hands-on tutorials. Real-World Data Mining will be valuable to professionals on analytics teams; professionals seeking certification in the field; and undergraduate or graduate students in any analytics program: concentrations, certificate-based, or degree-based.

IBM Business Process Manager V8.5 Performance Tuning and Best Practices


Author: Mike Collins,Zi Hui Duan,Andreas Fried,Ben Hoflich,Chris Richardson,Torsten Wilms,IBM Redbooks
Publisher: IBM Redbooks
ISBN: 0738440418
Category: Computers
Page: 208
View: 8274
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This IBM® Redbooks® publication provides performance tuning tips and best practices for IBM Business Process Manager (IBM BPM) V8.5.5 (all editions) and IBM Business Monitor V8.5.5. These products represent an integrated development and runtime environment based on a key set of service-oriented architecture (SOA) and business process management (BPM) technologies. Such technologies include Service Component Architecture (SCA), Service Data Object (SDO), Business Process Execution Language (BPEL) for web services, and Business Processing Modeling Notation (BPMN). Both IBM Business Process Manager and Business Monitor build on the core capabilities of the IBM WebSphere® Application Server infrastructure. As a result, Business Process Manager solutions benefit from tuning, configuration, and best practices information for WebSphere Application Server and the corresponding platform Java virtual machines (JVMs). This book targets a wide variety of groups, both within IBM (development, services, technical sales, and others) and customers. For customers who are either considering or are in the early stages of implementing a solution incorporating Business Process Manager and Business Monitor, this document proves a useful reference. The book is useful both in terms of best practices during application development and deployment and as a reference for setup, tuning, and configuration information. This book talks about many issues that can influence performance of each product and can serve as a guide for making rational first choices in terms of configuration and performance settings. Similarly, customers who already implemented a solution with these products can use the information presented here to gain insight into how their overall integrated solution performance can be improved.