FORECASTING STARTUP SUCCESS OR FAILURE USING CRUNCHBASE DATA
J.VASAVI, M.PRIYANKA, C.SATHWIKA, J.VAMSHI, M.PREM KUMAR, MR.K.SUNIL KUMAR
ABSTRACT
Startups play a crucial role in driving innovation and economic growth, but they also encounter significant challenges, with a considerable proportion failing within their initial years. The traditional method of forecasting startup success or failure involves decision-makers relying on historical financial data, market analysis, and subjective evaluations of the founding team and business concept. While these approaches do provide some insights, they are limited in scalability, subject to human biases, and struggle to handle large volumes of diverse data. Consequently, the accuracy and reliability of traditional methods are compromised, leading to less effective identification of potential successful startups or missed opportunities to prevent investment in failing ventures. The necessity for accurate and efficient startup success prediction is of utmost importance for various stakeholders, including investors, entrepreneurs, and policymakers. Investing in early-stage startups involves high risks, and Artificial Intelligence (AI) can play a vital role in identifying high-potential startups, reducing the likelihood of financial losses. Entrepreneurs can greatly benefit from AI-based forecasting as it helps them assess their venture's viability, refine their strategies, and improve their chances of achieving long-term success. Policymakers can also leverage AI-driven insights to create targeted and effective support programs that foster a thriving startup ecosystem. AI has emerged as a promising solution to enhance the accuracy and efficiency of startup forecasting. AI is capable of processing vast amounts of structured and unstructured data, including social media trends, industry reports, and company news, with remarkable speed and precision. By uncovering hidden patterns and adapting to dynamic market conditions, AI enables better-informed decision-making in the ever-competitive startup landscape. This project focuses on using AI to predict startup success or failure by analyzing data from Crunchbase, a comprehensive database of startup information. The proposed system highlights the significance of AI in this context, underscoring its potential benefits for investors, entrepreneurs, and policymakers alike. Through AI, this system aims to revolutionize startup forecasting, providing valuable insights that empower all stakeholders involved in the startup ecosystem.
