Start-Up Capital Estimation and Profitability Prediction for E-Commerce Start-Ups Using Machine Learning

E-commerce start-ups play a pivotal role in today’s digital economy. Accurate estimation of startup capital and reliable profitability predictions are crucial for their success. In this study, we propose a novel approach that leverages machine learning techniques, specifically linear regression, to address these challenges. Our methodology involves creating a comprehensive dataset by collecting relevant business metrics from existing e-commerce start-ups. We then apply linear regression models to predict both start-up capital requirements and future profitability. By analyzing historical data, we identify key factors that significantly impact these metrics. Our findings demonstrate the effectiveness of our approach in providing actionable insights for entrepreneurs and investors. The resulting machine learning model can guide decision-making, optimize resource allocation, and enhance the overall sustainability of e-commerce ventures. This paper contributes to the growing field of business analytics and provides a valuable framework for assessing and supporting e-commerce start-ups. Our results underscore the importance of data-driven decision-making in the dynamic landscape of digital entrepreneurship.