Fire accounts for a significant portion of total property losses. High severity and low frequency, fire losses are inherently volatile, which makes modeling them difficult. This dataset enables more accurate identification of each policyholder’s risk exposure and the ability to tailor the insurance coverage for their specific operation
This dataset contains 105,450 insurance records.The target variable is a transformed ratio of loss to total insured value, and the explanatory variable contain policy characteristics, information on crime rate, geodemographics, and weather.
In this case we have a continuous variable as a target, so it is a regression task. To evaluate the results of this competition the organizers used the weighted Gini coefficient, where the weights are given by the var11 variable.
Although this dataset can make a huge difference on the insurance business' performance, it has some problems that complicate its usage. Luckily, Synthesized can solve these problems in a fast and intuitive way.
The data is available in the Kaggle competition "Liberty Mutual Group - Fire Peril Loss Cost"