The given notebook has been created by Dr. Michaela Baumann, who won the individual competition of the Data Science Challenge 2021 of the DAV.
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. First, this data set is read in and examined in detail. In the next step, a well-visualized and insightful exploratory data analysis is performed. In the subsequent data preparation for predictive modeling, automated variable preselection using the random forest method is performed and discussed. The subsequent binary classification (fraud detection) uses logistic regression and a "gradient boosting machine". Finally, some predictions of these models are comprehensively visualized using the method for Local Interpretable Model-agnostic Explanations "LIME".
The German Association of Actuaries (Deutsche Aktuarvereinigunge.V., DAV) is the professional representation of all actuaries in Germany. It was founded in 1993 and has more than 5,900 members today. More than 700 members are involved in thirteen committees and in over 60 working groups as a voluntary commitment.
The Data Science Challenge is an initiative of the Actuarial Data Science Committee of the DAV to encourage the engagement with machine learning and data science within the insurance industry.
Please note that the repositories provided on GitHub are published by the DAV. The content of linked websites is the sole responsibility of their operators. The DAV is not responsible for the code and data linked and referred to in the repositories.