Startup success prediction in the AI industry

Abstract

As Artificial intelligence is used in various industries, the necessity of AI research at the enterprise level is emerging. According to this trend, companies using AI are showing a sharp increase and M&A of large companies is increasing rapidly. As a result, venture companies that are challenging start-ups and existing companies that want to introduce AI are looking at startups that use artificial intelligence to jump into the AI industry. In this paper, a successful prediction model for startups with artificial intelligence through data analysis. The success forecasting model will help startups and investors gain a better understanding of the success of high-risk startups in this latest trend. Examine Crunchbase's database of the largest start-up data in the United States and accurately classify which startups are successful by building a predictive model through supervised learning using data from all startups with AI, not just in one country. With advances in information technology, robust machine characterization using complex machine learning algorithms can provide reliable results for data analysis. The success prediction model proposed in this study achieves a 95% forecast rate and the capacity to predict success is a very important competitive advantage for companies seeking to absorb venture capital and AI because they can see ahead of potential companies from an investment perspective.