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Python_fuer_Aktuare
Public.github
PublicADS_Use_Cases
Public- 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.
- 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.
- 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.
- 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 calculati…
claim_frequency
PublicGLM, Neural Network and Gradient Boosting for Insurance Pricing, Part 1: Claim Frequencyinsurance_scr_data
PublicMortality_Modeling
PublicMulti-Population Mortality Modeling With Neural Networks