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

Predictive Analytics & Risk Modeling: For Insurance and Actuarial Science

Chapter 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.

This chapter is in the full direct PDF edition. The free online sample covers the Introduction, Chapter 0 and References.

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Free companion material for this chapter:

Datasets and R setup for this chapter

Install the packages used in this chapter (free, from CRAN):

install.packages(c("tidyverse", "ExamPAData", "randomForest", "glmnet"))

exam_pa_titanic: ExamPAData package: MIT license (CRAN). The original Titanic passenger list is public historical data; the ‘titanic’ CRAN package version is CC0. Source: https://cran.r-project.org/package=ExamPAData

install.packages("ExamPAData")
library(ExamPAData)
data(exam_pa_titanic)

Sources and further reading

More from the author: predictiveinsightsai.com