Discover how to use advanced techniques for non-life pricing such as regression models and calibrating machine learning models in R through Notebooks.
If you’re looking to learn how to utilise advanced applications to pricing in R, this course is ideal for you. An individual subscription gives you 3 months’ online access to:
As Well As
Our Industry and Actuartech Resource Libraries which feature curated additional content to assist you on your data science journey.
The course starts with an explanation of the methodology and implementation for generalised additive models (GAMs) in R. We use an introductory example of moving from statistical models to machine learning models, followed by modeling continuous variables using GAMs and penalised regression techniques (e.g. lasso, ridge, elastic net, etc).
We then, in interactive e-learning sessions, discuss the difference between artificial intelligence (AI) and machine learning (ML) as well as classical approaches vs machine learning. We explain the objectives, families and general process/methodology of ML, and offer examples of ML in insurance. The first interactive e-learning session includes examples of ML in insurance. Thereafter, we focus on error measures, regression trees, bagging, and random forest. Lastly, we do a deep dive into gradient boosted models, neural networks, and support vector machines. Each interactive e-learning session ends with a short quiz for the student to check their understanding of the session before continuing.
The course ends with the live lesson which deals with calibrating a machine learning model. There is a reference to three practical experiences, the example and two hands-on case studies. The example showcases the application of a regression tree on the claim frequency, and the case studies are hands-on experiences of predicting the number of claims with a Gradient Boosting Machine (GBM) and prediction of random forest on average claim amount. The Notebooks, along with their respective memos, are available for completion on the platform.
This course is presented mainly through a combination of videos with slides, interactive e-learning slides and Jupyter Notebooks. After each video there is a short quiz for the student to gauge their understanding of the section before continuing.
Chapter 1 introduces modelling continuous explanatory variables with generalised additive models and penalised regression techniques.
Chapter 2 introduces machine learning and discusses supervised machine learning techniques.
Chapter 2 offers practical examples of the models as a Jupyter Notebook.
Chapter 3 offers an example of the prediction of the number of claims with a regression tree and includes a hands-on case study on predicting the number of claims with a GBM and using random forest to predict average claim amount.
Chapter 4 discusses how to calibrate a ML model in practice through cross-validation and parameter tuning.
The Appendix contains further resources to assist the student in their data science journey.
We have tailored packages available to ensure that corporate teams have the option to attend structured live lessons by our tutors, and the option to request a practical assignment and bespoke feedback. Invoicing option available.
