The code to create the dataset is available here. The dataset used in the paper is available on github

  • Maker - Categorical - The brand of the vehicle.
  • GenModel - Categorical - The model of the vehicle.
  • Color - Categorical - Colour of the vehicle.
  • Reg_Year - Categorical - Year of Registration.
  • Body_Type - Categorical - Eg. SUV, Convertible.
  • Runned_Miles - Numerical - Distance covered by the vehicle.
  • Engin_Size - Categorical - Size of engine.
  • GearBox - Categorical - Automatic, Manual.
  • FuelType - Categorical - Petrol, Diesel.
  • Price - Numerical - Price of vehicle.
  • Seat_num - Numerical - Number of seats.
  • Door_num - Numerical - Number of Doors.
  • issue - Categorical - Type of damage.
  • issue_id - Categorical - Specific damage.
  • repair_complexity - Categorical - Difficulty to repair the vehicle.
  • repair_hours - Numerical - Time required to finish the job.
  • repair_cost - Numerical - Cost of repair.

Other attributes are not used for evaluation in this work. breakdown_date and repair_date were added with the idea of inserting anomalies based on the number of days required to repair the vehicle.

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