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
