Given some descriptors of a car and its price, the goal of this problem is to predict the degree to which the car is riskier than its price indicates. Actuaries in the insurance business call this process symboling, and the outcome is a rank: a value of +3 indicates the car is risky; -3 indicates that it's pretty safe (although the lowest value in the dataset is -2).

The description of the car includes its specifications in terms of various characteristics (brand, fuel type, body style, length, and so on). Moreover, you get its price and normalized loss in use as compared to other cars (this represents the average loss per car per year, normalized for all cars within a certain car segment).

There are 205 cars in the dataset, and the number of features is 25; some of them are categorical, and others are numerical. In addition, the dataset expressively states that there are some missing values, encoded using the string "?".

Although it is not stated directly on the presentation...