The dataset contains eBay auction information on Cartier wristwatches. There are 7 variables (including dependent variable).

auctionid - unique identifier of an auction
bid - the proxy bid placed by a bidder
bidtime - the time (in days) that the bid was placed, from the start of the auction
bidder - eBay username of the bidder
bidderrate - eBay feedback rating of the bidder
openbid - the opening bid set by the seller
price - the closing price that the item sold for (equivalent to the second highest bid + an increment)

To load the training data in your jupyter notebook, use the below command:

import pandas as pd

auction_data = pd.read_csv("https://raw.githubusercontent.com/dphi-official/Datasets/master/auction_data/train_set_label.csv" )

You can find more details on how to save a prediction file here: https://discuss.dphi.tech/t/how-to-submit-predictions/548

Sample submission: You should submit a CSV file with a header row and the sample submission can be found below

prediction

164

761

174

594

144

1760

.

.

Etc.

Note that the header name should be prediction else it will throw an evaluation error. A sample submission file can be found here

Load the test data (name it as test_data). You can load the data using the below command.

test_data = pd.read_csv('https://raw.githubusercontent.com/dphi-official/Datasets/master/auction_data/test_set_label.csv')

Here the target column is deliberately not there as you need to predict it

The dataset is sourced from modeling online auctions.

Data Sprint #17: Online Auction