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

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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.

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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.

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