Start-Up Success Prediction Analysis Using Hybrid Machine Learning Technique
Keywords:
Gradient Boosting, Hybrid Model, K-Nearest Neighbours, Logistic Regression, Random Forest, Naïve Bayes, Start-up, Support Vector MachineAbstract
This paper introduces a novel approach utilizing hybrid machine learning algorithms to predict start-up success. Acknowledging the inherent risks in the start-up landscape, we aim to demystify the perception of high failure rates associated with new ventures. Leveraging data from diverse sources, this paper’s methodology integrates pre-processing, feature selection, and hybrid model construction. By combining algorithms such as Logistic Regression, K-Nearest Neighbours, Random Forest, Naive Bayes, Gradient Boosting, and Support Vector Machine, this paper’s approach achieves an accuracy of up to 96.22%. Real-world experimentation validates the robustness and scalability of this paper’s predictive model, offering stakeholders valuable insights for informed decision-making in the entrepreneurial ecosystem.
Downloads
References
Baskoro H, Prabowo H, Meyliana M, Gaol FL. Predicting startup success, a literature review. InProceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) 2022 Feb 28 (Vol. 1, No. 1, pp. 51-57).
Gangwani D, Zhu X. Modeling and prediction of business success: a survey. Artificial Intelligence Review. 2024 Feb;57(2):1-51.
Varma S. Machine Learning based Outcome Prediction of New Ventures: A review. vol.;9:529-32.
Hsairi L. Deep Learning to Predict Start-Up Business Success. International Journal of Advanced Computer Science & Applications. 2024 Mar 1;15(3).
Leary MM, DeVaughn ML. Entrepreneurial team characteristics that influence the successful launch of a new venture. Management Research News. 2009 Apr 24;32(6):567-79.
Triono SP, Rahayu A, Wibowo LA, Alamsyah A. Factors Affecting Start-up Performance. In6th Global Conference on Business, Management, and Entrepreneurship (GCBME 2021) 2022 Jul 12 (pp. 529-534). Atlantis Press.
Silva Júnior CR, Siluk JC, Neuenfeldt Júnior A, Rosa CB, Michelin CD. Overview of the factors that influence the competitiveness of startups: a systematized literature review. Gestão & Produção. 2022 Sep 9;29:e13921.
Ferrati F, Muffatto M. Entrepreneurial finance: emerging approaches using machine learning and big data. Foundations and Trends® in Entrepreneurship. 2021 Apr 27;17(3):232-329.
Balu N. Indian Start-ups’ Success Prediction Using Machine Learning (Doctoral dissertation, Dublin, National College of Ireland).
Shetty S, Sundaram R, Achuthan K. Assessing and comparing top accelerators in Brazil, India, and the USA: through the lens of new ventures’ performance. Entrepreneurial Business and Economics Review. 2020 Jun 30;8(2):153-77.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
