  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.
