Editorial
Decision Tree Based Crowd Funding for Kickstarter Projects
@ARTICLE{10.4108/eetsis.4639, author={Veena Grover and A. Anbarasi and Siddesh Fuladi and M. K. Nallakaruppan}, title={Decision Tree Based Crowd Funding for Kickstarter Projects}, journal={EAI Endorsed Transactions on Scalable Information Systems}, volume={11}, number={2}, publisher={EAI}, journal_a={SIS}, year={2023}, month={12}, keywords={Kickstarter, Decision Tree, Crowdfunding}, doi={10.4108/eetsis.4639} }- Veena Grover
A. Anbarasi
Siddesh Fuladi
M. K. Nallakaruppan
Year: 2023
Decision Tree Based Crowd Funding for Kickstarter Projects
SIS
EAI
DOI: 10.4108/eetsis.4639
Abstract
The proposed work employs the C4.5 decision tree algorithm on a kick-starter project dataset to help a user decide whether to back a kick-starter project that is ongoing by predicting how likely it is that it may be a successful one. We pre-processed the kick-starter dataset with about 35 columns, and used WEKA to run the algorithm on the dataset. We reached an accuracy of 99.7% and we also talk about why the algorithm chose 5 particular attributes over the others. A lot of other papers have discussed this problem from a project creator’s standpoint, predicting whether a project is going to be a success before it has begun. There are fewer papers which look into predicting the success of the ongoing projects that helps users choose potentially successful projects to back, and we have also achieved a higher accuracy rate.
Copyright © 2023 V. Grover et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
