deutscheaktuarvereinigung Goto Github PK
Type: Organization
Location: Cologne, Germany
Blog: www.aktuar.de
Type: Organization
Location: Cologne, Germany
Blog: www.aktuar.de
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
Notebooks of the eXplainableAI working group of the German actuarial association
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TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
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Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
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Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.