Call for Paper - Upcoming Issues

Using Machine Learning to Predict Sales Conditional on Bid Acceptance

International Journal of Economics and Management Studies
© 2019 by SSRG - IJEMS Journal
Volume 6 Issue 11
Year of Publication : 2019
Authors : Barry E. King, Jason Davidson
10.14445/23939125/IJEMS-V6I11P101
pdf
How to Cite?

Barry E. King, Jason Davidson, "Using Machine Learning to Predict Sales Conditional on Bid Acceptance," SSRG International Journal of Economics and Management Studies, vol. 6,  no. 11, pp. 1-3, 2019. Crossref, https://doi.org/10.14445/23939125/IJEMS-V6I11P101

Abstract:

A North American provider of vehicle parking solutions seeks to predict if a bid will be successful and, for those that are successful, what will be the cumulative sales revenue. Both traditional statistical methods and machine learning algorithms were employed. The machine learning techniques performed better than the statistical methods. There is no statistically significant difference between random forest and extreme gradient boosting for either the binary classification task or the regression task.

Keywords:

logistic regression, linear regression, random forest, extreme gradient boosting, Tukey honestly significant test

References:

[1] G. Janson, What Machine Learning Approaches Have Won Most Kaggle Competitions?, Retrieved from https://www.quora.com/What-machine-learning-approacheshave-won-most-Kaggle-competitions.