Predicting the Success of Startups Based on Investment and Funding Details

Startup companies are an essential element of the contemporary corporate environment, serving as a significant driver of both technological advancement and economic expansion. On the other hand, a large percentage of these new businesses never attain sustained success, which is why it is crucial to precisely anticipate their future possibilities. It may be difficult for startups to thrive, grow, and accomplish their goals if they do not have access to sufficient money and investments. The amount invested, the amount funded, and the achievements of the business are highly important characteristics that may be used to forecast the success of any startup. The primary objective of this research is to accurately predict whether a startup is conducive to success or not, based on its historic information about investment rounds, invested companies, investment dates, funding raised, funding rounds, funding dates, milestones achieved, milestones date, and so on. U sing a variety of machine learning algorithms analysis of historical is conducted. A number of other crucial aspects, such as the amount of time it took to get funding and investments, as well as the contacts that the firm has been taken into consideration. Using different classification and regression methods, an exhaustive comparison of accuracy, an area under the ROC curve, and other such performance metrics are carried out in order to determine which model is the most accurate. This research offers a clear understanding of the success of any startup to its stakeholders such as entrepreneurs, investors, funders, and policymakers by examining previous similar studies.