STARTUPFATE: PREDICTING SUCCESS FOR STARTUPS WITH MACHINE LEARNING

Abstract

Startup failure is a significant concern in the business landscape, with most new ventures facing closure within their first few years. Identifying factors that contribute to startup failure can assist investors in making informed decisions, entrepreneurs in mitigating risks, and policymakers in creating supportive environments. This project aims to address this problem by leveraging historical startup data and developing a predictive model to identify startups at risk of failure. We have shown that Artificial Intelligence can leverage publicly available data on the Internet to calculate the probability of each of these outcomes with a high level of confidence. The idea for this project came to us considering the failure of Pakistan's largest startup, "Airlift." Witnessing the rise and fall of such a significant player in the startup landscape prompted us to introspect, aiming to not only learn from their missteps but also to contribute something unique and valuable to the entrepreneurial ecosystem. Our project aims to develop an Artificial Intelligence model capable of predicting the likelihood of a startup's success or failure based on relevant features and factors. This project culminates in the creation of a user-friendly website where users can input startup information and receive predictions, aiding entrepreneurs, and investors in making informed decisions