University Presentation Showcase: Graduate Division
Developing Web-based Predictive Application for Crowdfunding Campaigns
Presenter Hometown
Nicholasville
Major
Applied Computing: Software Engineering and Cyber Security
Department
Computer Science
Degree
Graduate
Mentor
Dae Wook Kim PhD.
Mentor Department
Computer Science
Recommended Citation
Cannon, David M. II; Dixon, Alex J.; and Holzapfel, Ethan B., "Developing Web-based Predictive Application for Crowdfunding Campaigns" (2020). University Presentation Showcase Event. 1.
https://encompass.eku.edu/swps/2020/graduate/1
Abstract
The popularity of crowdfunding campaigns like Kickstarter, GoFundMe, and Indiegogo has led to the rise of the campaign creators and backers to predict if the campaigns are likely to succeed prior to launching them via the Internet. Predicting a successful campaign which implies the funding goal is reached can have an important influence on the campaign project description including USD pledged, number of backers, goal amount, amount of days, and campaign category, etc. in the crowdfunding platform. Therefore, we studied 300,000 real campaigns of 2009-2016 Kickstarter in Kaggle competition to find the determinant features for the campaign’s success and applied a machine learning algorithm to develop a web-based application that enables a user to predict if a campaign is a successful or failed project. Our application can provide insights to creators and backers to better understand practical impact of a crowdfunding campaign.
Presentation format
Poster
Developing Web-based Predictive Application for Crowdfunding Campaigns
The popularity of crowdfunding campaigns like Kickstarter, GoFundMe, and Indiegogo has led to the rise of the campaign creators and backers to predict if the campaigns are likely to succeed prior to launching them via the Internet. Predicting a successful campaign which implies the funding goal is reached can have an important influence on the campaign project description including USD pledged, number of backers, goal amount, amount of days, and campaign category, etc. in the crowdfunding platform. Therefore, we studied 300,000 real campaigns of 2009-2016 Kickstarter in Kaggle competition to find the determinant features for the campaign’s success and applied a machine learning algorithm to develop a web-based application that enables a user to predict if a campaign is a successful or failed project. Our application can provide insights to creators and backers to better understand practical impact of a crowdfunding campaign.
