deutscheaktuarvereinigung
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PK
Type: User
Company: Deutsche Aktuarvereinigung e. V. (DAV)
Bio: The given repositories have been created by committees of the DAV and serve as an aid for actuaries and interested persons to support them in their work.
Location: Cologne, Germany
Blog: www.aktuar.de
deutscheaktuarvereinigung's Projects
Notebooks etc. for Actuarial Data Science use cases
GLM, Neural Network and Gradient Boosting for Insurance Pricing, Part 1: Claim Frequency
The notebook on the main topic of interpretable machine learning is a descriptive and instructive analysis of a car data set from a public source.
The study Machine-Learning Methods for Insurance Applications is dedicated to the question of how new developments in the collection of data and their evaluation in the context of Data Science in the actuarial world can be utilized. The results of the study are based on the R language, so the first goal of this work is to reproduce the calculations described in the Jupyter notebook in the Python programming language and to compare the results with those of the study authors. Besides these presented methods we continue to work on a random forest. Therefore, our second goal is the development of an artificial neural network, which has at least a similar quality compared to the other machine learning methods.
In this notebook we take a look at a relevant project that is frequently encountered by insurers: Fraud Detection. For this purpose we use a car data set from a public source and will show the necessary steps to establish an automated fraud detection.
In this Python notebook, based on a large French. The results are compared and the interpretability of the models is analyzed and evaluated with SHAP and PDP plots. In addition, the four tools TPOT, Auto-Sklearn, H2O and FLAML are tested or used.
Deriving of a NHANES-data set (CDC) for a mortality analysis
Modeling and Forecasting using Affectedness Variables
How to Work With Comprehensive Internal Model Data for Three Portfolios
Multi-Population Mortality Modeling With Neural Networks