Woohoo! This competition has come to a close!
Many thanks to the participants for all of their hard work and commitment to using data for good!
Can you predict whether a donor will return to donate blood given their donation history? This is the smallest, least complex dataset on DrivenData, and a great place to dive into the world of data science competitions.
The Blood Transfusion Service Center Dataset
The UCI Machine Learning Repository is a great resource for practicing your data science skills. They provide a wide range of datasets for testing machine learning algorithms. Finding a subject matter you're interested in can be a great way to test yourself on real-world data problems. Given our mission, we're interested in predicting if a blood donor will donate within a given time window.
Here's what the first few rows of the training set look like:
|Months since Last Donation||Number of Donations||Total Volume Donated (c.c.)||Months since First Donation||Made Donation in March 2007|
Predict if the donor will give in March 2007
The goal is to predict the last column, whether he/she donated blood in March 2007.
Use information about each donor's history
Months since Last Donation: this is the number of monthis since this donor's most recent donation.
Number of Donations: this is the total number of donations that the donor has made.
Total Volume Donated: this is the total amound of blood that the donor has donated in cubuc centimeters.
Months since First Donation: this is the number of months since the donor's first donation.
This competitions uses log loss as its evaluation metric, so the predictions you submit are the probability that a donor made a donation in March 2007.
The submission format is a csv with the following columns:
|Made Donation in March 2007|
To be explicit, you need to submit a file like the following with predictions for every ID in the Test Set we provide:
,Made Donation in March 2007 659,0.5 276,0.5 263,0.5 303,0.5 ...
Data is courtesy of Yeh, I-Cheng via the UCI Machine Learning repository:
Yeh, I-Cheng, Yang, King-Jang, and Ting, Tao-Ming, "Knowledge discovery on RFM model using Bernoulli sequence, "Expert Systems with Applications, 2008, doi:10.1016/j.eswa.2008.07.018.
Warm Up: Predict Blood Donations: Rules and Terms of Data Use
DrivenData Competition Rules
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Private sharing of code
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Public dissemination of entries
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Open licensing of winners
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Documentation for winning solutions
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External data is not allowed unless otherwise noted explicitly on these competition pages. Participants agree to make no attempt to use additional data or data sources not provided on these competition pages.
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This Competition is a challenge of skill and the final results are determined solely by leaderboard ranking on the private leaderboard (subject to compliance with these Competition Rules). Participants' scores and ranks on the Competition Website at any given stage of the Competition will be based on the evaluation metric described on the Competition Website, as determined by applying the predictions in the Submission to the ground truth of a validation dataset whose instances were a fixed set sampled from the Data.
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