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Gettam Rajgopal, Chandrashekar (2023) Optimizing Startup Funding Predictions: Genetic Programming and Machine Learning Synergy. Masters thesis, Dublin, National College of Ireland.
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Abstract
Startup finance is a vital and significant area that makes a considerable contribution to innovation and economic progress in the startup finance industry. Predicting startup funding accurately is important not just for investors, entrepreneurs and policymakers but also has a significant impact on the shaping economic landscape. This project delves into the application of genetic programming to optimize various machine learning models for the prediction of total funding in startup investments and addresses the difficulty of estimating total funding which is a task made more difficult by the complexity of startup ecosystems by analyzing a comprehensive dataset on startup investments which contains various financial indicators like seed, equity, venture and funding rounds. In this study, different machine learning models like Symbolic Regression, Random Forest, Linear Regression, XGBoost and K-Nearest Neighbors (KNN) are used using Genetic programming, which yields more precise prediction of startup funding forecasts and deeper insights through distinct viewpoints and gaps that have been found. Random Forest has performed better in predicting total funding when compared to other models. This research aims not only to contribute to the academic field of financial predictive analytics but also to provide reasonable tools for stakeholders in the startup and venture capital sectors and eventually aid in more informed decision-making processes.
| Item Type: | Thesis (Masters) |
|---|---|
| Supervisors: | Name Email Jain, Mayank UNSPECIFIED |
| Subjects: | H Social Sciences > HG Finance Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning H Social Sciences > HD Industries. Land use. Labor > New Business Enterprises |
| Divisions: | School of Computing > Master of Science in Data Analytics |
| Depositing User: | Ciara O'Brien |
| Date Deposited: | 08 May 2025 11:37 |
| Last Modified: | 08 May 2025 11:37 |
| URI: | https://norma.ncirl.ie/id/eprint/7515 |
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