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Field Lab Yunoai: startup analytics - a machine learning approach to predict startup success based on founders´ features
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| 6.3 MB | Adobe PDF |
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Resumo(s)
This thesis examines the impact of founders on startup success, focusing on education,
professional experience, and LinkedIn metrics. It analyzes university and workplace prestige,
including consultancy or VC experience, and LinkedIn follower count as a connectivity
measure. Using advanced text analysis with SBERT and TF-IDF embeddings, it evaluates
LinkedIn posts and profiles—a novel approach in predicting startup success. Founder
personality traits are predicted through X platform data linked to LinkedIn profiles via
Crunchbase. A three-layered success definition offers a comprehensive framework for
evaluating outcomes, providing new insights into the relationship between founder
characteristics and startup success.
Descrição
Palavras-chave
Startup success Funding metrics Failure prediction Founder-Market Fit (FMF) Machine learning Natural Language Processing (NLP) Startup dynamics