Abstract

This research proposes an algorithmic machine learning framework aimed at the early evaluation of business ideas. The framework integrates fifteen critical variables organized into five dimensions-innovation, sustainability, the entrepreneurial team, scalability, and initial finances-identified from a systematic review of the literature. Unlike traditional approaches that focus on financial metrics or one-dimensional indicators, this model provides a comprehensive, multidimensional view of entrepreneurial viability in uncertain contexts. Methodologically, the study presents a structured pipeline that incorporates Random Forest, Gradient Boosting, and XGBoost ensemble algorithms, as well as SMOTE data balancing techniques. These techniques address common problems, such as class imbalance and generalization limitations. Theoretically, innovation and sustainability constructs are operationalized alongside entrepreneurial and financial factors, contributing to more consistent, integrative evaluation models. In practical terms, this proposal provides incubators, accelerators, and public policy designers with a replicable and adaptable tool for the early stages of entrepreneurship. While empirical validation is planned for the future, this work lays the methodological groundwork to bridge gaps in the literature and advance more robust predictive models for entrepreneurial evaluation.