• G. Pravalika
• K.Keerthana
• M.Poojitha
• A.Rekha

This study has been undertaken to predict the success of startups, is a critical yet challenging task,. In this study, we propose a simple abstract model for predicting startup success based on key factors such as market demand, team expertise, funding, and innovation. By analyzing historical data of successful and failed startups, we develop a predictive framework using machine learning algorithms. Our model aims to provide early-stage investors and entrepreneurs with valuable insights into the potential success of a startup, enabling informed decision-making and resource allocation. Through empirical validation and continuous refinement, our approach offers a promising avenue for enhancing the efficiency and accuracy of startup evaluation processes, ultimately contributing to the sustainability and growth of the entrepreneurial ecosystem.

Early-Stage Investors, Market Demand, Entrepreneurial Ecosystem, Innovation, Deep Learning

"Startup Sucess Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.k170-k174, March-2024, Available :http://www.jetir.org/papers/JETIR2403A20.pdf

"Startup Sucess Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. ppk170-k174, March-2024, Available at : http://www.jetir.org/papers/JETIR2403A20.pdf