Applying Predictive Analytics: Finding Value in Data

By Mary M. McCarthy, Richard V. McCarthy, Wendy Ceccucci, et al.

Book Overview

Chapter 1

Introduction to Predictive Analytics1

1.1 Predictive Analytics in Action2

1.2 Analytics Landscape8

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 239

Know Your Data - Data Preparation39

2.1 Classification of Data40

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(TM) 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 35

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 Testing2

3.8 Analysis of Variance (ANOVA)5

3.9 Chi Square6

3. Fit Statistics8

3. Stochastic Models9

3.12 Chapter Summary1

References2

Chapter 4

Predictive Models Using Regression5

4.1 Regression6

4.1.1 Classical assumptions7

4.2 Ordinary Least Squares8

4.3 Simple Linear Regression8

4.3.1 Determining Relationship Between Two Variables9

4.3.2 Line of Best Fit and Simple Linear Regression Equation9

4.4 Multiple Linear Regression1

4.4.1 Metrics to Evaluate the Strength of the Regression Line2

4.3.2 Best-fit model3

4.3.3 Selection of Variables in Regression3

4.5 Principal Component Regression5

4.5.1 Principal Component Analysis Revisited5

4.5.2 Principal Component Regression6

4.6 Partial Least Squares6

4.7 Logistic Regression7

4.7.1 Binary Logistic Regression8

4.7.2 Examination of Coefficients1

4.7.3 Multinomial Logistic Regression3

4.7.4 Ordinal Logistic Regression3

4.8 Implementation of Regression in SAS Enterprise Miner(TM)3

4.8.1 Regression Node Train Properties: Class Targets4

4.8.2 Regression Node Train Properties: Model Options5

4.8.3 Regression Node Train Properties: Model Selection6

4.9 Implementation of Two-Factor Interaction and Polynomial Terms8

4.9.1 Regression Node Train Properties: Equation8

4. DMINE Regression in SAS Enterprise Miner(TM)0

4..1 DMINE Properties0

4..2 DMINE Results2

4. Partial Least Squares Regression in SAS Enterprise Miner(TM)4

4..1 Partial Least Squares Properties4

4..2 Partial Least Squares Results7

4. Least Angles Regression in SAS Enterprise Miner(TM)9

4..1 Least Angle Regression Properties0

4..2 Least Angles Regression Results1

4. Other Forms of Regression4

4. Chapter Summary6

References9

Chapter 5

The Second of the Big Three - Decision Trees1

5.1 What is a Decision Tree?2

5.2 Creating a Decision Tree4

5.3 Data Partitions and Decision Trees6

• Format: Hardcover
• Language: English
• ISBN: 3030830691
• ISBN13: 9783030830694
• Release Date: December 2021
• Publisher: Springer
• Length: 274 Pages
• Weight: 1.29 lbs.
• Dimensions: 0.7" x 6.1" x 9.2"