Data
shill-bidding

shill-bidding

active ARFF BSD (from scikit-learn) Visibility: public Uploaded 19-05-2021 by Meilina Reksoprodjo
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We scraped a large number of eBay auctions of a popular product. After preprocessing the auction data, we build the SB dataset. The goal is to share the labelled SB dataset with the researchers.

13 features

Class (target)numeric2 unique values
0 missing
Record_IDnumeric6321 unique values
0 missing
Auction_IDnumeric807 unique values
0 missing
Bidder_IDstring1054 unique values
0 missing
Bidder_Tendencynumeric489 unique values
0 missing
Bidding_Rationumeric400 unique values
0 missing
Successive_Outbiddingnumeric3 unique values
0 missing
Last_Biddingnumeric5807 unique values
0 missing
Auction_Bidsnumeric49 unique values
0 missing
Starting_Price_Averagenumeric22 unique values
0 missing
Early_Biddingnumeric5690 unique values
0 missing
Winning_Rationumeric72 unique values
0 missing
Auction_Durationnumeric5 unique values
0 missing

19 properties

6321
Number of instances (rows) of the dataset.
13
Number of attributes (columns) of the dataset.
0
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
12
Number of numeric attributes.
0
Number of nominal attributes.
0
Percentage of instances having missing values.
0.81
Average class difference between consecutive instances.
0
Percentage of missing values.
0
Number of attributes divided by the number of instances.
92.31
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.

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