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claim_frequency
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Deriving-NHANES-data-set-CDC-for-mortality-analysis
Deriving-NHANES-data-set-CDC-for-mortality-analysis PublicDeriving of a NHANES-data set (CDC) for a mortality analysis
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WorkingGroup_eXplainableAI_Notebooks
WorkingGroup_eXplainableAI_Notebooks PublicNotebooks of the eXplainableAI working group of the German actuarial association
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Data_Science_Challenge_2020_Betrugserkennung
Data_Science_Challenge_2020_Betrugserkennung PublicIn 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 nece…
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- WorkingGroup_eXplainableAI_Notebooks Public
Notebooks of the eXplainableAI working group of the German actuarial association
DeutscheAktuarvereinigung/WorkingGroup_eXplainableAI_Notebooks’s past year of commit activity - Deriving-NHANES-data-set-CDC-for-mortality-analysis Public
Deriving of a NHANES-data set (CDC) for a mortality analysis
DeutscheAktuarvereinigung/Deriving-NHANES-data-set-CDC-for-mortality-analysis’s past year of commit activity - Data_Science_Challenge_2022_Python-Notebook_zur_Erstellung_von_Schadenhaeufigkeitsmodellen 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.
DeutscheAktuarvereinigung/Data_Science_Challenge_2022_Python-Notebook_zur_Erstellung_von_Schadenhaeufigkeitsmodellen’s past year of commit activity - Data-Science-Challenge2021_Explainable-Machine-Learning Public
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.
DeutscheAktuarvereinigung/Data-Science-Challenge2021_Explainable-Machine-Learning’s past year of commit activity - Data_Science_Challenge_2020_Betrugserkennung Public
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.
DeutscheAktuarvereinigung/Data_Science_Challenge_2020_Betrugserkennung’s past year of commit activity - Data_Science_Challenge_2020_Berufsunfaehigkeit Public
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…
DeutscheAktuarvereinigung/Data_Science_Challenge_2020_Berufsunfaehigkeit’s past year of commit activity - claim_frequency Public
GLM, Neural Network and Gradient Boosting for Insurance Pricing, Part 1: Claim Frequency
DeutscheAktuarvereinigung/claim_frequency’s past year of commit activity
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