Publicação
Field Llab YunoAI: startup analytics a machine learning analysis for predicting startup failure
| Resumo: | This study examines startup failure prediction by identifying critical risk factors and providing actionable strategies to mitigate challenges. Utilizing advanced machine learning models, including Random Forest and Gradient Boosting, the study emphasizes recall as the primary metric to accurately detect at-risk startups. Unlike prior research, which often focuses on success predictors, this study shifts the focus to failure dynamics, introducing a novel multidimensional approach that incorporates funding patterns, company age, and industry-specific factors. By addressing data limitations and integrating dynamic datasets, this research offers innovative frameworks and insights to deepen the understanding of startup sustainability. It provides a valuable resource for stakeholders. |
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| Autores principais: | Diaz, Sebastian |
| Assunto: | Startup failure prediction Machine learning models Operational metrics Risk factors analysis |
| 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 study examines startup failure prediction by identifying critical risk factors and providing actionable strategies to mitigate challenges. Utilizing advanced machine learning models, including Random Forest and Gradient Boosting, the study emphasizes recall as the primary metric to accurately detect at-risk startups. Unlike prior research, which often focuses on success predictors, this study shifts the focus to failure dynamics, introducing a novel multidimensional approach that incorporates funding patterns, company age, and industry-specific factors. By addressing data limitations and integrating dynamic datasets, this research offers innovative frameworks and insights to deepen the understanding of startup sustainability. It provides a valuable resource for stakeholders. |
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