Shill Bidding Dataset
Donated on 3/9/2020
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.
Dataset Characteristics
Multivariate
Subject Area
Computer Science
Associated Tasks
Classification, Clustering
Feature Type
-
# Instances
6321
# Features
13
Dataset Information
Has Missing Values?
No
Variable Information
Record ID: Unique identifier of a record in the dataset. Auction ID: Unique identifier of an auction. Bidder ID: Unique identifier of a bidder. Bidder Tendency: A shill bidder participates exclusively in auctions of few sellers rather than a diversified lot. This is a collusive act involving the fraudulent seller and an accomplice. Bidding Ratio: A shill bidder participates more frequently to raise the auction price and attract higher bids from legitimate participants. Successive Outbidding: A shill bidder successively outbids himself even though he is the current winner to increase the price gradually with small consecutive increments. Last Bidding: A shill bidder becomes inactive at the last stage of the auction (more than 90\% of the auction duration) to avoid winning the auction. Auction Bids: Auctions with SB activities tend to have a much higher number of bids than the average of bids in concurrent auctions. Auction Starting Price: a shill bidder usually offers a small starting price to attract legitimate bidders into the auction. Early Bidding: A shill bidder tends to bid pretty early in the auction (less than 25\% of the auction duration) to get the attention of auction users. Winning Ratio: A shill bidder competes in many auctions but hardly wins any auctions. Auction Duration: How long an auction lasted. Class: 0 for normal behaviour bidding; 1 for otherwise.
Dataset Files
| File | Size |
|---|---|
| Shill Bidding Dataset.csv | 537.4 KB |
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset shill_bidding_dataset = fetch_ucirepo(id=562) # data (as pandas dataframes) X = shill_bidding_dataset.data.features y = shill_bidding_dataset.data.targets # metadata print(shill_bidding_dataset.metadata) # variable information print(shill_bidding_dataset.variables)
Shill Bidding Dataset [Dataset]. (2020). UCI Machine Learning Repository. https://doi.org/10.24432/C5Z611.
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.