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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