Actuarial Data Science Tutorials
On this page we present all the tutorials that have been prepared by the working party. We are intensively working on additional ones and we aim to have approx. 10 tutorials, covering a wide range of Data Science topics relevant for actuaries.
All tutorials consist of an article and the corresponding code. In the article, we describe the methodology and the statistical model. By providing you with the code you can easily replicate the analysis performed and test it on your own data.
Case Study 10: LocalGLMnet: a deep learning architecture for actuaries
(Code on GitHub will follow)
Case Study 9: Convolutional neural network studies: (1) anomalies in mortality rates (2) image recognition
Case Study 8: Peeking into the Black Box: An Actuarial Case Study for Interpretable Machine Learning
R Code on GitHub ; R Markdown on GitHub
Case Study 7: The Art of Natural Language Processing: Classical, Modern and Contemporary Approaches to Text Document Classification
Case Study 6: Lee and Carter go Machine Learning: Recurrent Neural Networks
Case Study 5: Unsupervised Learning: What is a Sports Car?
Code on GitHub ; R Markdwon on GitHub
Case Study 4: On Boosting: Theory and Applications
Case Study 3: Nesting Classical Actuarial Models into Neural Networks
Case Study 2: Insights from Inside Neural Networks
Case Study 1: French Motor Third-Party Liability Claims
Code on GitHub, consisting of standard R, R using TensorFlow and R using H2O.
