Publicação
Field lab YunoAI: startup analytics-a machine learning analysis of acquisition and IPO likelihood
| Resumo: | This thesis explores the drivers of startup success through a data-driven, multidimensional approach. First, it examines funding metrics to identify the strongest predictors of outcomes like follow-on rounds, acquisitions, or IPOs. Second, machine learning models analyze failure dynamics, pinpointing critical risk factors to improve failure prediction. Third, founder-market fit (FMF) is quantified using NLP techniques and LinkedIn data, producing an objective FMF score to guide venture capital decisions. Lastly, founder-specific traits—including education, experience, and networks—are assessed through advanced text analysis and personality predictions. By integrating machine learning with entrepreneurial insights, this research provides actionable tools for stakeholders. |
|---|---|
| Autores principais: | Gonchar, Ekaterina |
| Assunto: | Startup success Funding metrics Failure prediction Founder-market fit (FMF) Machine learning Natural language processing (NLP) Startup dynamics |
| Ano: | 2025 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | This thesis explores the drivers of startup success through a data-driven, multidimensional approach. First, it examines funding metrics to identify the strongest predictors of outcomes like follow-on rounds, acquisitions, or IPOs. Second, machine learning models analyze failure dynamics, pinpointing critical risk factors to improve failure prediction. Third, founder-market fit (FMF) is quantified using NLP techniques and LinkedIn data, producing an objective FMF score to guide venture capital decisions. Lastly, founder-specific traits—including education, experience, and networks—are assessed through advanced text analysis and personality predictions. By integrating machine learning with entrepreneurial insights, this research provides actionable tools for stakeholders. |
|---|