Date of Publication :3rd October 2024

Abstract:In recent years, the application of machine learning (ML) models has gained significant traction in different fields, such as the prediction of startup success. Startups face many challenges, and the ability to predict their success or failure is o f paramount importance for investors, entrepreneurs, and stakeholders. This paper provides a comprehensive literature review of existing research on the use of ML models in predicting the success of startups. By examining a wide array of studies, this review highlights the key factors influencing startup success, such as financial performance, team composition, market conditions, and business models. Various ML algorithms—such as logistic regression, decision trees, support vector machines (SVM), and deep learning techniques—have been employed across these studies. The review also explores the datasets, features, and evaluation metrics commonly used in predicting outcomes. This paper aims to synthesize the state of the art in this field and identify current trends, challenges, and future research opportunities.

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