Authors: Bandaru Udayasree
Abstract: The rapid growth of e-commerce startups has created significant opportunities for innovation and economic development; however, a large proportion of these ventures fail due to inadequate financial planning and uncertain profitability. Accurate estimation of start-up capital requirements and early prediction of business profitability are therefore essential for entrepreneurs, investors, and financial institutions. This research presents a machine learning-based framework for estimating start-up capital and predicting the profitability of e-commerce startups using historical business and financial data. The proposed system analyzes critical parameters such as funding amount, investment history, operational expenses, revenue projections, market trends, and business characteristics to identify patterns associated with successful and profitable ventures. Multiple machine learning algorithms, including Decision Tree, Random Forest, Gradient Boosting, Logistic Regression, and Multi-Layer Perceptron (MLP), are trained and evaluated to determine the most effective prediction model. Data preprocessing techniques such as feature selection, handling missing values, and normalization are applied to improve model performance and reliability. Experimental results demonstrate that the proposed framework achieves high prediction accuracy, enabling data-driven decision-making for startup planning and investment evaluation. The developed system provides an intelligent decision support tool that assists entrepreneurs in estimating initial capital requirements, assessing business profitability, minimizing financial risk, and improving the likelihood of long-term business success in the competitive e-commerce ecosystem.
