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E-book: Applying Predictive Analytics: Finding Value in Data

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  • Format: EPUB+DRM
  • Pub. Date: 01-Jan-2022
  • Publisher: Springer Nature Switzerland AG
  • Language: eng
  • ISBN-13: 9783030830700
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Applying Predictive Analytics: Finding Value in DataLarger Image
  • Format: EPUB+DRM
  • Pub. Date: 01-Jan-2022
  • Publisher: Springer Nature Switzerland AG
  • Language: eng
  • ISBN-13: 9783030830700
Other books in subject:

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The 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.

1 Introduction to Predictive Analytics
1(26)
1.1 Predictive Analytics in Action
2(4)
1.2 Analytics Landscape
6(5)
1.2.1 Big Data
7(4)
1.3 Analytics
11(1)
1.3.1 Predictive Analytics
11(1)
1.4 Regression Analysis
12(1)
1.5 Machine Learning Techniques
13(4)
1.6 Predictive Analytics Model
17(2)
1.7 Opportunities in Analytics
19(2)
1.8 Introduction to the Automobile Insurance Claim Fraud Example
21(2)
1.9
Chapter Summary
23(4)
References
25(2)
2 Know Your Data: Data Preparation
27(28)
2.1 Classification of Data
27(2)
2.1.1 Qualitative Versus Quantitative
28(1)
2.1.2 Scales of Measurement
28(1)
2.2 Data Preparation Methods
29(3)
2.2.1 Inconsistent Formats
30(1)
2.2.2 Missing Data
30(1)
2.2.3 Outliers
31(1)
2.2.4 Other Data Cleansing Considerations
32(1)
2.3 Data Sets and Data Partitioning
32(1)
2.4 SAS Enterprise Miner™ Model Components
32(21)
2.4.1 Step
1. Create Three of the Model Components
33(2)
2.4.2 Step
2. Import an Excel File and Save as a SAS File
35(3)
2.4.3 Step
3. Create the Data Source
38(4)
2.4.4 Step
4. Partition the Data Source
42(2)
2.4.5 Step
5. Data Exploration
44(1)
2.4.6 Step
6. Missing Data
44(3)
2.4.7 Step
7. Handling Outliers
47(4)
2.4.8 Step
8. Categorical Variables with Too Many Levels
51(2)
2.5
Chapter Summary
53(2)
Reference
54(1)
3 What Do Descriptive Statistics Tell Us
55(32)
3.1 Descriptive Analytics
56(1)
3.2 The Role of the Mean, Median, and Mode
56(1)
3.3 Variance and Distribution
57(3)
3.4 The Shape of the Distribution
60(5)
3.4.1 Skewness
60(1)
3.4.2 Kurtosis
61(4)
3.5 Covariance and Correlation
65(2)
3.6 Variable Reduction
67(11)
3.6.1 Variable Clustering
68(7)
3.6.2 Principal Component Analysis
75(3)
3.7 Hypothesis Testing
78(1)
3.8 Analysis of Variance (ANOVA)
79(1)
3.9 Chi-Square
80(1)
3.10 Fit Statistics
81(1)
3.11 Stochastic Models
82(1)
3.12
Chapter Summary
83(4)
References
85(2)
4 Predictive Models Using Regression
87(36)
4.1 Regression
88(1)
4.1.1 Classical Assumptions
88(1)
4.2 Ordinary Least Squares
89(1)
4.3 Simple Linear Regression
90(1)
4.3.1 Determining Relationship Between Two Variables
90(1)
4.3.2 Line of Best Fit and Simple Linear Regression Equation
90(1)
4.4 Multiple Linear Regression
91(4)
4.4.1 Metrics to Evaluate the Strength of the Regression Line
92(1)
4.4.2 Best-Fit Model
93(1)
4.4.3 Selection of Variables in Regression
93(2)
4.5 Principal Component Regression
95(1)
4.5.1 Principal Component Analysis Revisited
95(1)
4.5.2 Principal Component Regression
95(1)
4.6 Partial Least Squares
95(1)
4.7 Logistic Regression
96(5)
4.7.1 Binary Logistic Regression
97(2)
4.7.2 Examination of Coefficients
99(1)
4.7.3 Multinomial Logistic Regression
100(1)
4.7.4 Ordinal Logistic Regression
100(1)
4.8 Implementation of Regression in SAS Enterprise Miner™
101(3)
4.8.1 Regression Node Train Properties: Class Targets
101(1)
4.8.2 Regression Node Train Properties: Model Options
102(1)
4.8.3 Regression Node Train Properties: Model Selection
102(2)
4.9 Implementation of Two-Factor Interaction and Polynomial Terms
104(2)
4.9.1 Regression Node Train Properties: Equation
105(1)
4.10 DMINE Regression in SAS Enterprise Miner™
106(3)
4.10.1 DMINE Properties
106(2)
4.10.2 DMINE Results
108(1)
4.11 Partial Least Squares Regression in SAS Enterprise Miner™
109(4)
4.11.1 Partial Least Squares Properties
109(2)
4.11.2 Partial Least Squares Results
111(2)
4.12 Least Angle Regression in SAS Enterprise Miner™
113(4)
4.12.1 Least Angle Regression Properties
114(1)
4.12.2 Least Angle Regression Results
115(2)
4.13 Other Forms of Regression
117(1)
4.14
Chapter Summary
118(5)
References
121(2)
5 The Second of the Big 3: Decision Trees
123(22)
5.1 What Is a Decision Tree?
123(2)
5.2 Creating a Decision Tree
125(1)
5.3 Classification and Regression Trees (CART)
126(1)
5.4 Data Partitions and Decision Trees
127(2)
5.5 Creating a Decision Tree Using SAS Enterprise Miner™
129(8)
5.5.1 Overfitting
136(1)
5.6 Creating an Interactive Decision Tree Using SAS Enterprise Miner™
137(3)
5.7 Creating a Maximal Decision Tree Using SAS Enterprise Miner™
140(3)
5.8
Chapter Summary
143(2)
References
144(1)
6 The Third of the Big 3: Neural Networks
145(30)
6.1 What Is a Neural Network?
145(2)
6.2 History of Neural Networks
147(2)
6.3 Components of a Neural Network
149(2)
6.4 Neural Network Architectures
151(2)
6.5 Training a Neural Network
153(1)
6.6 Radial Basis Function Neural Networks
154(1)
6.7 Creating a Neural Network Sing SAS Enterprise Miner™
155(7)
6.8 Using SAS Enterprise Miner™ to Automatically Generate a Neural Network
162(6)
6.9 Explaining a Neural Network
168(3)
6.10
Chapter Summary
171(4)
References
173(2)
7 Model Comparisons and Scoring
175(24)
7.1 Beyond the Big 3
175(1)
7.2 Gradient Boosting
176(2)
7.3 Ensemble Models
178(2)
7.4 Random Forests
180(1)
7.5 Memory-Based Reasoning
181(3)
7.6 Two-Stage Model
184(1)
7.7 Comparing Predictive Models
185(5)
7.7.1 Evaluating Fit Statistics: Which Model Do We Use?
187(3)
7.8 Using Historical Data to Predict the Future: Scoring
190(5)
7.8.1 Analyzing and Reporting Results
191(2)
7.8.2 Save Data Node
193(1)
7.8.3 Reporter Node
194(1)
7.9 The Importance of Predictive Analytics
195(2)
7.9.1 What Should We Expect for Predictive Analytics in the Future?
196(1)
7.10
Chapter Summary
197(2)
References
198(1)
8 Finding Associations in Data Through Cluster Analysis
199(34)
8.1 Applications and Uses of Cluster Analysis
199(1)
8.2 Types of Clustering Techniques
200(1)
8.3 Hierarchical Clustering
200(11)
8.3.1 Agglomerative Clustering
201(5)
8.3.2 Divisive Clustering
206(4)
8.3.3 Agglomerative Versus Divisive Clustering
210(1)
8.4 Non-hierarchical Clustering
211(10)
8.4.1 K-Means Clustering
211(4)
8.4.2 Initial Centroid Selection
215(1)
8.4.3 Determining the Number of Clusters
216(3)
8.4.4 Evaluating Your Clusters
219(2)
8.5 Hierarchical Versus Non-hierarchical
221(1)
8.6 Cluster Analysis Using SAS Enterprise Miner™
221(3)
8.6.1 Cluster Node
222(1)
8.6.2 Additional Key Properties of the Cluster Node
222(2)
8.7 Applying Cluster Analysis to the Insurance Claim Fraud Data Set
224(7)
8.8
Chapter Summary
231(2)
References
232(1)
9 Text Analytics: Using Qualitative Data to Support Quantitative Results
233(22)
9.1 What Is Text Analytics?
234(1)
9.2 Information Retrieval
235(2)
9.3 Text Parsing
237(3)
9.4 Zipf's Law
240(1)
9.5 Text Filter
241(2)
9.6 Text Cluster
243(3)
9.7 Text Topic
246(3)
9.8 Text Rule Builder
249(2)
9.9 Text Profile
251(1)
9.10
Chapter Summary
252(3)
References
254(1)
Appendix A Data Dictionary for the Automobile Insurance Claim Fraud Data Example 255(2)
Appendix B Can You Predict the Money Laundering Cases? 257(8)
References 265(2)
Index 267
Richard V. McCarthy (DBA, Nova Southeastern University, MBA, Western New England College) is a professor of Computer Information Systems at the School of Business, Quinnipiac University. Prior to this, Dr. McCarthy was an associate professor of management information systems at Central Connecticut State University. He has twenty years of experience within the insurance industry and has held a Charter Property Casualty Underwriter (CPCU) designation since 1991. He has authored numerous research articles and contributed to several textbooks. He has served as the associate dean of the School of Business, the MBA director, and the director of the Master of Science in Business Analytics program. In 2019, he was awarded the Computer Educator of the Year from the International Association for Computer Information Systems. 







Wendy Ceccucci (PhD and MBA, Virginia Polytechnic University) is a Professor and Chair of Computer Information Systems at QuinnipiacUniversity.  Her teaching areas include business analytics and programming. She is the past president of the Education Special Interest Group (EDSIG) of the Association for Information Technology Professionals (AITP) and past Associate Editor of the Information Systems Education Journal (ISEDJ). Her research interests include Information Systems Pedagogy. 





Mary McCarthy (DBA, Nova Southeastern University, MBA, University of Connecticut) is a professor and chair of Accounting, Central Connecticut State University. She has twenty years of financial reporting experience and has served as the controller for a Fortune 50 industry organization. She holds a CPA and CFA designation.  She has authored numerous research articles.
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