Forecasting the Success of Startups Using Machine Learning Methods

Student: Ezhova Darya

Faculty: St.Petersburg School of Economics and Management

Educational Programme: Economics (Bachelor)

Year of Graduation: 2024

Investing in startups, even at the stage of prolonged existence, is risky. Evaluating the likelihood of a startup’s success by investors often relies on qualitatively unmeasurable factors and their decision-making experience, which is susceptible to cognitive biases. Our research aims to de- velop a machine-learning model for investment decision-making. The machine learning methods we will use in our work are based on predicting the probability of a startup’s success based on qualitative and quantitative characteristics. The data used in our work contain information about various startups from around the world and different business sectors.

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