Abstract:

This article is a systematic literature search that compares methods used to determine the success rate of startups. It is a systematic literature review that serves, as a theoretical starting point, for further research in the field. It describes and compares different models for assessing the success rate of startups. The compared models are based on machine learning. The literature search was conducted on articles from the WoS and Scopus databases. Finally, the paper compares three studies that were compared based on two comparative metrics: accuracy and AUC. The comparison resulted in the evaluation of the best-performing model, which was Gradient Boosting.