An efficient and sustainable novel approach for prediction of start-up company success rates through sustainable machine learning paradigms

• Published in: E3S web of conferences 2023-01, Vol.430, p.1086
• Main Authors: Panduri, Bharathi, Abhilash, P.K., K., Chidananda, Naga Tejaswi Bethapud, Venkata, Naudiyal, Anjali, Kodamunja, Mahitha
• Format: Article
• Language: English
• ISSN: 2267-1242; 2267-1242
• DOI: 10.1051/e3sconf/202343001086

The primary objective is to construct a sustainable machine-learning model that utilizes multiple variables to forecast the success of a startup enterprise. It incorporates a Flask application for creating a user-friendly interface, where users can input specific parameters related to a startup, such as financial metrics, industry sector, and location. These inputs are then passed through a sustainable machine learning prediction model, which has been trained on a comprehensive dataset of startup information. The model employs sustainable advanced algorithms to evaluate their startup ventures' potential success. Through the development and deployment of the Flask application and the integration of sustainable machine learning prediction model, this model contributes to the field of startup analysis and decision-making. It offers a sustainable and efficient solution for predicting startup success, empowering users to make data-backed decisions and optimize their resource allocation.