Decision Tree Based Crowd Funding for Kickstarter Projects
DOI:
https://doi.org/10.4108/eetsis.4639Keywords:
Kickstarter, Decision Tree, CrowdfundingAbstract
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.
References
Patil, S., Mehta, J., Salunkhe, H., & Shah, H. (2021). Kickstarter Project Success Prediction and Classification Using Multi-layer Perceptron. https://doi.org/10.1007/978-981-33-4087-9_60
Kuppuswamy, V., & Bayus, B. (2015). Crowdfunding Creative Ideas: The Dynamics of Project Backers in Kickstarter. SSRN. Retrieved from https://ssrn.com/abstract=2234765
Hornuf, L., & Cumming, D. (Eds.). (2017). The Economics of Crowdfunding: Startups, Portals, and Investor Behavior. Forthcoming. SSRN. Retrieved from https://ssrn.com/abstract=2234765 or http://dx.doi.org/10.2139/ssrn.2234765
Agyeah, G., Mark, B., Adesiyan, J., & Kolomoytseva, A. (2019). Modeling the Success of Kickstarter Projects.
Kaggle. (2019). Kickstarter Dataset. Retrieved from https://www.kaggle.com/tayoaki/kickstarter-dataset
Kickstarter. (2019). Retrieved from https://www.kickstarter.com
Kaggle. (2019). Retrieved from https://www.kaggle.com/datasets
Quinlan, J. C. (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann.
Wikipedia. (2019). Statistical Classification. Retrieved from https://en.wikipedia.org/wiki/Statistical_classification
Hssina, B., Merbouha, A., Ezzikouri, H., & Erritali, M. (2014). A Comparative Study of Decision Tree ID3 and C4.5. International Journal of Advanced Computer Science and Applications, 4(2).
Frank, E., Hall, M. A., & Holmes, G. (2016). The WEKA Workbench. Online Appendix for Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann.
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Copyright (c) 2023 Veena Grover, A. Anbarasi, Siddesh Fuladi, M. K. Nallakaruppan
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