Data
kick

kick

active ARFF Public Domain (CC0) Visibility: public Uploaded 16-08-2018 by Janek Thomas
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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).

33 features

IsBadBuy (target)nominal2 unique values
0 missing
PurchDatenumeric517 unique values
0 missing
Auctionnominal3 unique values
0 missing
VehYearnumeric10 unique values
0 missing
VehicleAgenumeric10 unique values
0 missing
Makenominal33 unique values
0 missing
Modelnominal1063 unique values
0 missing
Trimnominal134 unique values
2360 missing
SubModelnominal863 unique values
8 missing
Colornominal16 unique values
8 missing
Transmissionnominal3 unique values
9 missing
WheelTypeIDnominal4 unique values
3169 missing
WheelTypenominal3 unique values
3174 missing
VehOdonumeric39947 unique values
0 missing
Nationalitynominal4 unique values
5 missing
Sizenominal12 unique values
5 missing
TopThreeAmericanNamenominal4 unique values
5 missing
MMRAcquisitionAuctionAveragePricenumeric10342 unique values
18 missing
MMRAcquisitionAuctionCleanPricenumeric11379 unique values
18 missing
MMRAcquisitionRetailAveragePricenumeric12725 unique values
18 missing
MMRAcquisitonRetailCleanPricenumeric13456 unique values
18 missing
MMRCurrentAuctionAveragePricenumeric10315 unique values
315 missing
MMRCurrentAuctionCleanPricenumeric11265 unique values
315 missing
MMRCurrentRetailAveragePricenumeric12493 unique values
315 missing
MMRCurrentRetailCleanPricenumeric13192 unique values
315 missing
PRIMEUNITnominal2 unique values
69564 missing
AUCGUARTnominal2 unique values
69564 missing
BYRNOnominal74 unique values
0 missing
VNZIP1nominal153 unique values
0 missing
VNSTnominal37 unique values
0 missing
VehBCostnumeric2010 unique values
68 missing
IsOnlineSalenominal2 unique values
0 missing
WarrantyCostnumeric281 unique values
0 missing

62 properties

72983
Number of instances (rows) of the dataset.
33
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
149271
Number of missing values in the dataset.
69709
Number of instances with at least one value missing.
14
Number of numeric attributes.
19
Number of nominal attributes.
0.12
First quartile of skewness among attributes of the numeric type.
90231265.95
Mean of means among attributes of the numeric type.
1473.43
First quartile of standard deviation of attributes of the numeric type.
0.79
Average class difference between consecutive instances.
0.01
Average mutual information between the nominal attributes and the target attribute.
2.33
Second quartile (Median) of entropy among attributes.
0.54
Entropy of the target attribute values.
291.68
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0.89
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Number of attributes divided by the number of instances.
127.05
Average number of distinct values among the attributes of the nominal type.
7382.16
Second quartile (Median) of means among attributes of the numeric type.
56.12
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.36
Mean skewness among attributes of the numeric type.
0
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
87.7
Percentage of instances belonging to the most frequent class.
1302587.07
Mean standard deviation of attributes of the numeric type.
0.3
Second quartile (Median) of skewness among attributes of the numeric type.
64007
Number of instances belonging to the most frequent class.
0.01
Minimal entropy among attributes.
12.12
Percentage of binary attributes.
2704.37
Second quartile (Median) of standard deviation of attributes of the numeric type.
7.93
Maximum entropy among attributes.
-1.22
Minimum kurtosis among attributes of the numeric type.
95.51
Percentage of instances having missing values.
4.68
Third quartile of entropy among attributes.
9.96
Maximum kurtosis among attributes of the numeric type.
4.18
Minimum of means among attributes of the numeric type.
6.2
Percentage of missing values.
1.61
Third quartile of kurtosis among attributes of the numeric type.
1263091913.59
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
42.42
Percentage of numeric attributes.
9924.54
Third quartile of means among attributes of the numeric type.
0.06
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
57.58
Percentage of nominal attributes.
0.01
Third quartile of mutual information between the nominal attributes and the target attribute.
1063
The maximum number of distinct values among attributes of the nominal type.
-0.45
Minimum skewness among attributes of the numeric type.
0.66
First quartile of entropy among attributes.
0.53
Third quartile of skewness among attributes of the numeric type.
2.07
Maximum skewness among attributes of the numeric type.
1.71
Minimum standard deviation of attributes of the numeric type.
-0.2
First quartile of kurtosis among attributes of the numeric type.
3329.14
Third quartile of standard deviation of attributes of the numeric type.
18196024.53
Maximum standard deviation of attributes of the numeric type.
12.3
Percentage of instances belonging to the least frequent class.
8976
Number of instances belonging to the least frequent class.
5098.02
First quartile of means among attributes of the numeric type.
299.8
Standard deviation of the number of distinct values among attributes of the nominal type.
2.81
Average entropy of the attributes.
4
Number of binary attributes.
0
First quartile of mutual information between the nominal attributes and the target attribute.
1.82
Mean kurtosis among attributes of the numeric type.

4 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: area_under_roc_curve - target_feature: IsBadBuy
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: IsBadBuy
0 runs - estimation_procedure: 33% Holdout set - target_feature: IsBadBuy
0 runs - estimation_procedure: 50 times Clustering
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