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001443591 001__ 1443591
001443591 003__ OCoLC
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001443591 019__ $$a1290814165$$a1290839295$$a1351995183
001443591 020__ $$a9783030830700$$q(electronic bk.)
001443591 020__ $$a3030830705$$q(electronic bk.)
001443591 020__ $$z9783030830694
001443591 020__ $$z3030830691
001443591 0247_ $$a10.1007/978-3-030-83070-0$$2doi
001443591 035__ $$aSP(OCoLC)1291316054
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001443591 049__ $$aISEA
001443591 050_4 $$aQA76.9.D343$$bM33 2021eb
001443591 08204 $$a006.3/12$$223
001443591 1001_ $$aMcCarthy, Richard V.
001443591 24510 $$aApplying predictive analytics :$$bfinding value in data /$$cRichard V. McCarthy, Mary M. McCarthy, Wendy Ceccucci.
001443591 250__ $$a2nd ed.
001443591 260__ $$aCham :$$bSpringer,$$c2021.
001443591 300__ $$a1 online resource (282 pages)
001443591 336__ $$atext$$btxt$$2rdacontent
001443591 337__ $$acomputer$$bc$$2rdamedia
001443591 338__ $$aonline resource$$bcr$$2rdacarrier
001443591 504__ $$aIncludes bibliographical references and index.
001443591 5050_ $$aChapter 1 -- Introduction to Predictive Analytics -- 1.1 Predictive Analytics in Action -- 1.2 Analytics Landscape -- 1.3 Analytics -- 1.3.2 Predictive Analytics -- 1.4 Regression Analysis -- 1.5 Machine Learning Techniques -- 1.6 Predictive Analytics Model -- 1.7 Opportunities in Analytics -- 1.8 Introduction to the Automobile Insurance Claim Fraud Example -- 1.9 Chapter Summary -- References -- Chapter 2 -- Know Your Data : Data Preparation -- 2.1 Classification of Data -- 2.1.1 Qualitative versus Quantitative -- 2.1.2 Scales of Measurement -- 2.2. Data Preparation Methods. -- 2.2.1 Inconsistent Formats -- 2.2.2 Missing Data -- 2.2.3 Outliers -- 2.2.4 Other Data Cleansing Considerations -- 2.3 Data Sets and Data Partitioning -- 2.4 SAS Enterprise Miner"!Model Components -- 2.4.1 Step 1. Create Three of the Model Components -- 2.4.2 Step 2. Import an Excel File and Save as a SAS File -- 2.4.3 Step 3. Create the Data Source -- 2.4.4 Step 4. Partition the Data Source -- 2.4.5 Step 5 Data Exploration -- 2.4.6 Step 6 Missing Data -- 2.4.7 Step 7. Handling Outliers -- 2.4.8 Step 8. Categorical Variables with Too Many Levels -- 2.5 Chapter Summary -- References -- Chapter 3 -- What do Descriptive Statistics Tell Us -- 3.1 Descriptive Analytics -- 3.2 The Role of the Mean, Median and Mode -- 3.3 Variance and Distribution -- 3.4 The Shape of the Distribution -- 3.4.2 Kurtosis -- 3.5 Covariance and Correlation -- 3.6 Variable Reduction -- 3.6.1 Variable Clustering -- 3.6.2 Principal Component Analysis -- 3.7 Hypothesis Testing -- 3.8 Analysis of Variance (ANOVA) -- 3.9 Chi Square -- 3. Fit Statistics -- 3. Stochastic Models -- 3.12 Chapter Summary -- References -- Chapter 4 -- Predictive Models Using Regression -- 4.1 Regression -- 4.1.1 Classical assumptions -- 4.2 Ordinary Least Squares -- 4.3 Simple Linear Regression -- 4.3.1 Determining Relationship Between Two Variables -- 4.3.2 Line of Best Fit and Simple Linear Regression Equation -- 4.4 Multiple Linear Regression -- 4.4.1 Metrics to Evaluate the Strength of the Regression Line -- 4.3.2 Best-fit model -- 4.3.3 Selection of Variables in Regression -- 4.5 Principal Component Regression -- 4.5.1 Principal Component Analysis Revisited -- 4.5.2 Principal Component Regression -- 4.6 Partial Least Squares -- 4.7 Logistic Regression -- 4.7.1 Binary Logistic Regression -- 4.7.2 Examination of Coefficients -- 4.7.3 Multinomial Logistic Regression -- 4.7.4 Ordinal Logistic Regression -- 4.8 Implementation of Regression in SAS Enterprise Miner"!-- 4.8.1 Regression Node Train Properties: Class Targets -- 4.8.2 Regression Node Train Properties: Model Options -- 4.8.3 Regression Node Train Properties: Model Selection -- 4.9 Implementation of Two-Factor Interaction and Polynomial Terms -- 4.9.1 Regression Node Train Properties: Equation -- 4. DMINE Regression in SAS Enterprise Miner"!-- 4..1 DMINE Properties -- 4..2 DMINE Results -- 4. Partial Least Squares Regression in SAS Enterprise Miner"!-- 4..1 Partial Least Squares Properties -- 4..2 Partial Least Squares Results -- 4. Least Angles Regression in SAS Enterprise Miner"!-- 4..1 Least Angle Regression Properties -- 4..2 Least Angles Regression Results -- 4. Other Forms of Regression -- 4. Chapter Summary -- References -- Chapter 5 -- The Second of the Big Three : Decision Trees -- 5.1 What is a Decision Tree? -- 5.2 Creating a Decision Tree -- 5.3 Data Partitions and Decision Trees -- 5.4 Creating a Decision Tree Using SAS Enterprise Miner"!-- The key properties include: -- Subtree Properties -- 5.4.1 Overfitting -- 5.5 Creating an Interactive Decision Tree using SAS Enterprise Miner "!-- 5.6 Creating a Maximal Decision Tree using SAS Enterprise Miner "!-- 5.7 Chapter Summary -- References -- Chapter 6 -- The Third of the Big Three - Neural Networks -- 6.1 What is a Neural Network? -- 6.2 History of Neural Networks -- 6.3 Components of a Neural Network -- 6.4 Neural Network Architectures -- 6.5 Training a Neural Network -- 6.6 Radial Basis Function Neural Networks -- 6.7 Creating a Neural Network using SAS Enterprise Miner -- 6.8 Using SAS Enterprise MinerÔ to Automatically Generate a Neural Network -- 6.9 Explaining a Neural Network -- 6. Chapter Summary -- References -- Chapter 7 -- Model Comparisons and Scoring -- 7.1 Beyond the Big -- 7.2 Gradient Boosting -- 7.3 Ensemble Models -- 7.4 Random Forests -- 7.6 Two-Stage Model -- 7.7 Comparing Predictive Models -- 7.7.1 Evaluating Fit Statistics : Which Model Do We Use? -- 7.8 Using Historical Data to Predict the Future Scoring -- 7.8.1 Analyzing and Reporting Results -- 7.8.2 Save Data Node -- 7.8.3 Reporter Node -- 7.9 The Importance of Predictive Analytics -- 7.9.1 What Should We Expect for Predictive Analytics in the Future? -- 7. Chapter Summary -- References -- Chapter 8 -- finding Associations in Data through Cluster Analysis -- 8.1 Applications and Uses of Cluster Analysis -- 8.2 Types of Clustering Techniques -- 8.3 Hierarchical Clustering -- 8.3.1 Agglomerative Clustering -- 8.3.2 Divisive Clustering -- 8.3.3 Agglomerative vs Divisive Clustering -- 8.4 Non-hierarchical clustering -- 8.4.1 K-means Clustering -- 8.4.2 Initial Centroid Selection -- 8.4.3 Determining the Number of Clusters -- 8.4.4 Evaluating your clusters -- 8.5 Hierarchical vs Nonhierarchical -- 8.6 Cluster Analysis using SAS Enterprise Miner"!-- 8.6.1 Cluster Node -- 8.6.2 Additional Key Properties of the Cluster Node -- 8.7 Applying Cluster Analysis to the Insurance Claim Fraud Data Set -- 8.8 Chapter Summary -- References -- -- Chapter 9 -- 9.1 What is Text Analytics? -- 9.2 Information Retrieval -- 9.3 Text Parsing -- 9.4 Zipf's Law -- 9.5 Text Filter -- 9.6 Text Cluster -- 9.7 Text Topic -- 9.8 Text Rule Builder -- 9.9 Text Profile -- 9. Chapter Summary -- Discussion Questions -- References -- Appendix A -- Data Dictionary for the Automobile Insurance Claim Fraud Data Example -- Appendix B -- Can you Predict the Money Laundering Cases? -- B.1 Introduction -- B.2. Business Problem -- B.3. Analyze Data -- B.4. Development and Optimization of a Best Fit Model -- B.5. Final Report -- References.
001443591 506__ $$aAccess limited to authorized users.
001443591 520__ $$aThe new edition of this textbook presents a practical, updated approach to predictive analytics for classroom learning. The authors focus on using analytics to solve business problems and compares several different modeling techniques, all explained from examples using the SAS Enterprise Miner software. The authors demystify complex algorithms to show how they can be utilized and explained within the context of enhancing business opportunities. Each chapter includes an opening vignette that provides real-life examples of how business analytics have been used in various aspects of organizations to solve issues or improve their results. A running case provides an example of a how to build and analyze a complex analytics model and utilize it to predict future outcomes. The new edition includes chapters on clusters and associations and text mining to support predictive models. An additional case is also included that can be used with each chapter or as a semester project.
001443591 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed January 13, 2022).
001443591 63000 $$aEnterprise miner$$vTextbooks.
001443591 650_0 $$aData mining.
001443591 650_0 $$aSAS (Computer program language)
001443591 650_6 $$aExploration de données (Informatique)
001443591 650_6 $$aSAS (Langage de programmation)
001443591 655_7 $$aTextbooks.$$2fast$$0(OCoLC)fst01423863
001443591 655_0 $$aElectronic books.
001443591 7001_ $$aMcCarthy, Mary M.$$c(College teacher)
001443591 7001_ $$aCeccucci, Wendy.
001443591 77608 $$iPrint version:$$aMcCarthy, Richard V.$$tApplying Predictive Analytics.$$dCham : Springer International Publishing AG, ©2021$$z9783030830694
001443591 852__ $$bebk
001443591 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-83070-0$$zOnline Access$$91397441.1
001443591 909CO $$ooai:library.usi.edu:1443591$$pGLOBAL_SET
001443591 980__ $$aBIB
001443591 980__ $$aEBOOK
001443591 982__ $$aEbook
001443591 983__ $$aOnline
001443591 994__ $$a92$$bISE