Dark Mode
Car Kick Prediction Data
Data Science and Analytics
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
This data product focuses on the significant challenge faced by auto dealerships when purchasing used vehicles at auctions: identifying "kicks," which are bad purchases. Kicked cars are problematic vehicles that often have mechanical issues, tampered odometers, or difficulties with obtaining the vehicle title, making them difficult or impossible to sell to customers. Such purchases are costly to dealers due to transportation fees, wasted repair efforts, and market losses. This dataset provides the necessary variables to build models that predict which cars carry a higher risk of being a bad buy, offering real value to dealerships attempting to secure the best possible inventory selection.
Columns
This data product contains 31 distinct columns detailing vehicle characteristics, acquisition costs, and status. Key columns include:
- PurchDate: The date the car was purchased.
- VehYear: The year the car was produced (primarily 2001 through 2010).
- VehicleAge: The age of the car (mean age is approximately 4.17 years).
- VehOdo: The distance the car has driven, measured in kilometers (mean is about 71.7k km).
- MMR Acquisition Prices: Various price points related to the car when it was bought at auction (average and clean prices).
- MMR Current Prices: Various current price points for the car at auction and retail (average and clean prices).
- VehBCost: The B price of the car (mean is about 6.75k).
- WarrantyCost: The cost associated with the car's warranty (mean is about 1.28k).
- Auction: The physical location where the auction took place (e.g., MANHEIM).
- Make: The producer of the car (CHEVROLET and DODGE are most frequent).
- Model/Trim/SubModel: Specific identification details of the vehicle.
- Color/Transmission/WheelType: Physical characteristics of the car.
- Nationality: The car's national origin (85% are AMERICAN).
- TopThreeAmericanName: Indicates if the car is from one of the three largest American manufacturers (GM and CHRYSLER are leading).
- VNZIP1/VNST: Geographical indicators related to the car's location (e.g., TX, FL).
- IsOnlineSale: Binary indicator showing if the sale occurred online.
- Class: The target variable for prediction, indicating the class of the car (0 or 1, signifying a bad buy or "kick").
Distribution
The data file is typically available in CSV format and is identified as
car_kick.csv, with a file size of 14.53 MB. The structure consists of 31 columns. The dataset contains approximately 67.2 thousand valid records across all key numerical and categorical features. The Purchase Dates span a range of Unix timestamps, roughly correlating to purchase activity between 2009 and 2010.Usage
This data product is perfectly suited for classification challenges. Ideal applications include:
- Developing machine learning models, such as Random Forest, to predict the binary outcome of whether an auctioned vehicle will become a "kick."
- Risk assessment and fraud detection in the used car industry.
- Informing procurement strategies for auto dealerships to minimise costly mistakes.
- Predictive analytics focused on mitigating financial losses linked to unforeseen vehicle issues.
Coverage
The dataset captures purchases occurring across various locations, identified by auction name (such as MANHEIM) and specific ZIP codes (
VNZIP1) and state abbreviations (VNST), with Texas (TX) and Florida (FL) being common states. The temporal coverage primarily spans the years 2009 to 2010, based on the Purchase Date records. The demographic scope centres on the US market, with 85% of the included vehicles originating from American manufacturers, most frequently GM and CHRYSLER.License
Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
Who Can Use It
- Data Scientists and Machine Learning Engineers: To create and benchmark predictive classification algorithms targeting auto auction risk.
- Auto Dealership Analysts: To integrate data-driven insights into their purchasing process and determine the risk level of potential inventory acquisitions.
- Researchers in Automotive Finance: To study the factors that contribute to high-risk vehicle purchases and subsequent financial losses.
Dataset Name Suggestions
- Car Kick Prediction Data
- Auto Auction Risk Analysis
- Used Vehicle Bad Buy Forecaster
- Auction Kick Classifier
Attributes
Original Data Source: Car Kick Prediction Data
QUALITYLoading...