A dataset from the Allstate Insurance company will be used, which consists of more than 300,000 examples with masked and anonymous data and consisting of more than 100 categorical and numerical attributes, thus being compliant with confidentiality constraints, more than enough for building and evaluating a variety of ML techniques.

The dataset is downloaded from the Kaggle website at https://www.kaggle.com/c/allstate-claims-severity/data. Each row in the dataset represents an insurance claim. Now, the task is to predict the value for the loss column. Variables prefaced with cat are categorical, while those prefaced with cont are continuous.

It is to be noted that the Allstate Corporation is the second largest insurance company in the United States, founded in 1931. We are trying to make the whole thing automated, to predict the cost, and hence the severity, of accident and damage claims.