Applying Predictive Analytics

Chapter 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 MinerÔ.- 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.