DEVELOPMENT... OpenML
Data
kick

kick

active ARFF Publicly available Visibility: public Uploaded 27-01-2023 by Smith
0 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
One of the biggest challenges of an auto dealership purchasing a used car at an auto auction is the risk of that the vehicle might have serious issues that prevent it from being sold to customers. The auto community calls these unfortunate purchases "kicks". Kicked cars often result when there are tampered odometers, mechanical issues the dealer is not able to address, issues with getting the vehicle title from the seller, or some other unforeseen problem. Kick cars can be very costly to dealers after transportation cost, throw-away repair work, and market losses in reselling the vehicle.Modelers who can figure out which cars have a higher risk of being kick can provide real value to dealerships trying to provide the best inventory selection possible to their customers.The challenge of this competition is to predict if the car purchased at the Auction is a Kick (bad buy).

31 features

class (target)numeric2 unique values
0 missing
Makenominal32 unique values
0 missing
IsOnlineSalenominal2 unique values
0 missing
VNSTnominal37 unique values
0 missing
VNZIP1nominal150 unique values
0 missing
BYRNOnominal72 unique values
0 missing
TopThreeAmericanNamenominal4 unique values
0 missing
Sizenominal12 unique values
0 missing
Nationalitynominal4 unique values
0 missing
WheelTypenominal3 unique values
0 missing
WheelTypeIDnominal3 unique values
0 missing
Transmissionnominal3 unique values
0 missing
Colornominal16 unique values
0 missing
SubModelnominal823 unique values
0 missing
Trimnominal133 unique values
0 missing
Modelnominal953 unique values
0 missing
PurchDatenumeric516 unique values
0 missing
Auctionnominal3 unique values
0 missing
WarrantyCostnumeric279 unique values
0 missing
VehBCostnumeric1981 unique values
0 missing
MMRCurrentRetailCleanPricenumeric12932 unique values
0 missing
MMRCurrentRetailAveragePricenumeric12239 unique values
0 missing
MMRCurrentAuctionCleanPricenumeric11057 unique values
0 missing
MMRCurrentAuctionAveragePricenumeric10162 unique values
0 missing
MMRAcquisitonRetailCleanPricenumeric13237 unique values
0 missing
MMRAcquisitionRetailAveragePricenumeric12516 unique values
0 missing
MMRAcquisitionAuctionCleanPricenumeric11200 unique values
0 missing
MMRAcquisitionAuctionAveragePricenumeric10185 unique values
0 missing
VehOdonumeric38057 unique values
0 missing
VehicleAgenumeric10 unique values
0 missing
VehYearnumeric10 unique values
0 missing
Show all 31 features

19 properties

NumberOfInstances
67212
Number of instances (rows) of the dataset.
NumberOfFeatures
31
Number of attributes (columns) of the dataset.
NumberOfClasses
0
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
15
Number of numeric attributes.
NumberOfSymbolicFeatures
16
Number of nominal attributes.
PercentageOfSymbolicFeatures
51.61
Percentage of nominal attributes.
AutoCorrelation
1
Average class difference between consecutive instances.
PercentageOfNumericFeatures
48.39
Percentage of numeric attributes.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfBinaryFeatures
3.23
Percentage of binary attributes.
NumberOfBinaryFeatures
1
Number of binary attributes.
MinorityClassSize
Number of instances belonging to the least frequent class.
MinorityClassPercentage
Percentage of instances belonging to the least frequent class.
MajorityClassSize
Number of instances belonging to the most frequent class.
MajorityClassPercentage
Percentage of instances belonging to the most frequent class.
Dimensionality
0
Number of attributes divided by the number of instances.
Show all 19 properties

1 tasks

Supervised Classification on kick
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Define a new task