DeutscheAktuarvereinigung/Data-Science-Challenge2021_Explainable-Machine-Learning

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DeutscheAktuarvereinigung/Data-Science-Challenge2021_Explainable-Machine-Learning

DeutscheAktuarvereinigung

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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.

claim_frequency

GLM, Neural Network and Gradient Boosting for Insurance Pricing, Part 1: Claim Frequency

Jupyter Notebook

Mortality_Modeling

Multi-Population Mortality Modeling With Neural Networks

Jupyter Notebook

insurance_scr_data

How to Work With Comprehensive Internal Model Data for Three Portfolios

Jupyter Notebook

Deriving-NHANES-data-set-CDC-for-mortality-analysis

Deriving of a NHANES-data set (CDC) for a mortality analysis

Jupyter Notebook

Data_Science_Challenge_2020_Betrugserkennung

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.

Jupyter Notebook

Data_Science_Challenge_2020_Berufsunfaehigkeit

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.