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Abstract: Crowd funding enables individuals or businesses to raise funds for their projects, ventures, or caused by tapping into a large pool of contributors. It democratizes access to capital, allowing creators to bypass traditional financial institutions and engage directly with their community or target audience. This method fosters innovation, empowers grassroots movements, and facilitates the realization of diverse ideas that might otherwise struggle to secure funding through conventional channels. Utilizing a rich dataset encompassing funding details, milestones, relationships, and geographical data of various startups, we embark on an in- depth analysis involving data preprocessing, feature engineering, and exploratory data analysis to uncover key success determinants. Employing advanced classifiers like LGBM, XG Boost and gradient Boosting, our model undergoes rigorous training and evaluation, with LGBM emerging as the top performer, achieving an accuracy of 90.48%. The analysis underscores the critical role of funding, and investor presence in forecasting startup success. This research not only equips stakeholders with a powerful predictive tool but also highlights significant features influencing startup outcomes, offering a novel perspective on strategic investment planning. Additionally, we developed an interactive frontend platform to complement our predictive model. Utilizing AngularJS, the platform serves as a gateway for startups to register their ventures, providing realtime access to predictive insights. Through intuitive forms, startups enter essential information such as geographical location, funding received, and sector of operation. Upon submission, the data is processed by our backend, which evaluates it against our machine learning model to predict success rates. This integration empowers startups with immediate insights and facilitates data-driven decision-making among investors, thereby fostering a more dynamic and informed startup ecosystem.
Pranav et al. (Fri,) studied this question.