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arXiv · 2004.12504

How Much Should I Pay? An Empirical Analysis on Monetary Prize in TopCoder

Mostaan Lotfalian Saremi·Razieh Saremi·Denisse Martinez-Mejorado
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Abstract

It is reported that task monetary prize is one of the most important motivating factors to attract crowd workers. While using expert-based methods to price Crowdsourcing tasks is a common practice, the challenge of validating the associated prices across different tasks is a constant issue. To address this issue, three different classifications of multiple linear regression, logistic regression, and K-nearest neighbor were compared to find the most accurate predicted price, using a dataset from the TopCoder website. The result of comparing chosen algorithms showed that the logistics regression model will provide the highest accuracy of 90% to predict the associated price to tasks and KNN ranked the second with an accuracy of 64% for K = 7. Also, applying PCA wouldn't lead to any better prediction accuracy as data components are not correlated.

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Mostaan Lotfalian Saremi, Razieh Saremi, Denisse Martinez-Mejorado. 2020-04-26. How Much Should I Pay? An Empirical Analysis on Monetary Prize in TopCoder. https://doi.org/10.1007/978-3-030-50732-9

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