Startup Unicorn Success Prediction Using Ensemble Machine Learning Algorithm

Every year, a large number of companies are created, most of them never succeed or even survive. Although many startups receive enormous investments, it is still unclear which startups will receive funding from venture capitalists. Understanding what factors contribute to corporate success, and predicting a company’s performance, are of utmost importance. Recently, methods based on machine learning have been employed for accomplishing this task. This paper presents an ensemble machine learning method for predicting startup unicorns that are likely to be successful in their future ventures. This research showcases machine learning algorithms capabilities in improving the effectiveness with regard to early stage startup prediction. The results show that the proposed ensemble machine learning algorithm outperforms traditional machine learning algorithms in terms of various performance metrics thereby demonstrating its potential in predicting startup unicorns that are likely to be successful and competitive in the global market.