Explore applications of advanced methods in non-life pricing in R, such as unsupervised machine learning algorithms and profitability and competition analysis through Notebooks.
If you’re looking to learn how to utilise R in non-life insurance, 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 data & feature selection and feature engineering in R. Thereafter, we take a deep dive into the interpretability of machine learning (ML) models. After each video there is a short quiz for the student to gauge their understanding of the section before continuing.
The next section is the e-learning section which introduces unsupervised machine learning algorithms, including partitioning and hierarchical, density-based clustering. The interactive e-learning session ends with a short quiz for the student to gauge their understanding of the session before continuing.
The course ends with the live lesson which introduces profitability and competition analysis. There is reference to four practical experiences, two examples and two hands-on case studies. The first example showcases the binning of continuous variables and data filtering, whereas the second is an example of vehicle categorisation. The case studies are hands-on experiences of interpreting results of ML algorithms and the detection of interaction between variables. The Notebooks, along with their respective memos, are available for completion on the platform.
Chapter 1 introduces data selection, pre-analysis, and feature selection, including data quality, pre-treatment, missing values, etc. The chapter also discusses interpretability of machine learning.
Chapter 2 offers an introduction to unsupervised ML algorithms.
Chapter 3 contains an example of binning continuous variables and data filtering as well as hands-on case studies on interpreting results of ML algorithms and detection of interactions between variables.
Chapter 4 discusses profitability and competition analysis, including an example of reverse engineering market prices.
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.
