Predictive Insights AI · Book
Contents
  1. Introduction
  2. Ch 0 · DataFest
  3. 1.1 Data exploration
  4. 1.2 Intro to modeling
  5. 1.3 GLMs
  6. 1.4 GLMs for classification
  7. 1.5 Classification metrics
  8. 1.6 More GLM topics
  9. 1.7 Variable selection
  10. 1.8 Tree-based models
  11. 1.9 Unsupervised learning
  12. Ch 2 · Cybersecurity
  13. 3.1 Health costs case study
  14. 3.2 Apartment applicants
  15. 3.3 Titanic case study
  16. Ch 4 · Tools
  17. Ch 5 · Seven mistakes
  18. Ch 6 · Entrepreneurship
  19. Wrap-up
  20. Appendix
  21. References
  22. PA R Study Manual
Book cover: Predictive Analytics & Risk Modeling: For Insurance and Actuarial Science

The online edition

Predictive Analytics & Risk Modeling: For Insurance and Actuarial Science

Samuel Castillo · Sam, the Predictive Analyst · Edited by Azizul Haque

You scanned the code: welcome. Read the free sample (Introduction, Chapter 0 and References) on your phone, and find the datasets, install commands and sources for every chapter. The full book is the direct PDF edition.

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Contents

  1. •Introduction: The Essentials of Predictive Analytics Free sample
    Why predictive analytics matters, what this book covers, and seven common mistakes on the SOA Exam PA.
  2. 0Chapter 0: Highlights from Five College DataFest Free sample
    Volunteering at the Five College DataFest and what the event teaches about real-world data analysis.
  3. 1.1Chapter 1.1: Data Exploration in R Full edition
    Technical foundations of AI and machine learning, starting with data exploration, ggplot2 graphs, dplyr, missing values and a real-world case study in R.
  4. 1.2Chapter 1.2: Introduction to Modeling: OLS, Regression vs Classification Full edition
    Modeling vocabulary and notation, ordinary least squares, regression vs classification and regression metrics, with an R example.
  5. 1.3Chapter 1.3: Generalized Linear Models (GLMs) for Regression Full edition
    GLMs for regression, link functions, and how to interpret coefficients with identity and log links.
  6. 1.4Chapter 1.4: GLMs for Classification: Logistic Regression and Link Functions Full edition
    Binary and count targets, logit/probit/cloglog links and a logistic regression example on auto claims data.
  7. 1.5Chapter 1.5: Classification Metrics: Confusion Matrix, ROC and AUC Full edition
    Confusion matrices, precision and recall, ROC curves and the area under the curve (AUC).
  8. 1.6Chapter 1.6: Additional GLM Topics: Residuals, Offsets, Interactions, Tweedie Full edition
    Residuals, log transforms, reference levels, interactions, offsets, Tweedie regression and link/distribution combinations.
  9. 1.7Chapter 1.7: GLM Variable Selection: Stepwise, Ridge, Lasso and Elastic Net Full edition
    Stepwise selection, penalized regression with ridge, lasso and elastic net, with worked R examples.
  10. 1.8Chapter 1.8: Tree-Based Models: Decision Trees, Random Forests and GBMs Full edition
    Decision trees, ensemble learning, random forests, gradient boosted trees and prediction intervals from quantile GBMs.
  11. 1.9Chapter 1.9: Unsupervised Learning: PCA, K-Means and Hierarchical Clustering Full edition
    Principal component analysis, k-means clustering and hierarchical clustering in R.
  12. 2Chapter 2: Applications in Cybersecurity and Data Privacy Full edition
    Securing predictive models, defending against AI-based threats, and data de-identification under GDPR and CCPA.
  13. 3.1Chapter 3.1: Personalized Insurance: Health Costs Case Study (GLM vs GBM) Full edition
    Insurance use cases for predictive analytics, and a worked health-costs case study comparing a GLM and a GBM in R.
  14. 3.2Chapter 3.2: Apartment Applicants Case Study Solution (GLM, Lasso, Random Forest) Full edition
    An exam-style case study: GLM, AIC feature selection, lasso, bagged trees and random forests on apartment application data.
  15. 3.3Chapter 3.3: Titanic Case Study Solution (Random Forest, Logistic Regression, Elastic Net) Full edition
    An exam-style Titanic survival case study using k-means, random forests, logistic regression and elastic net.
  16. 4Chapter 4: Technical Tools and Practical Insights for R Full edition
    R programming, essential libraries and time-saving tips.
  17. 5Chapter 5: Top Seven Predictive Modeling Mistakes and How to Avoid Them Full edition
    Seven common mistakes in data engineering and predictive modeling, from poor communication to bad statistical assumptions.
  18. 6Chapter 6: Entrepreneurial Insights: Turning Data Skills into a Business Full edition
    How to turn data, analytics and predictive modeling skills into a profitable business.
  19. •Wrap-Up: Personal Reflections and Technical Mastery Full edition
    Key lessons and reflections.
  20. •Appendix: R Shortcuts, Data Issues and Model Evaluation Reference Full edition
    RStudio shortcuts, data issues and resolutions, model evaluation, GLM, penalized regression, tree and unsupervised learning reference notes.
  21. •References Free sample
    References and acknowledgements.
  22. +PA R Study Manual
    Free companion manual for the SOA Exam PA, in R.

Visit predictiveinsightsai.com for videos, research notes and courses. Watch the Models Explained series on YouTube.