A Framework to Predicting Startup Success Growth With Multiple Algorithms

Startups operate in highly dynamic environments characterized by uncertainty, limited resources, and intense competition, making the prediction of their success and growth a complex yet crucial task. This paper proposes a novel framework for predicting startup success and growth by employing multiple machine learning algorithms in a comparative and ensemble-based approach. The framework integrates heterogeneous features including financial indicators (e.g., funding stages, revenue growth), team characteristics (e.g., founder experience, diversity), market dynamics (e.g., customer adoption, partnerships), and macroeconomic trends. Several algorithms such as Logistic Regression, Random Forest, Gradient Boosting Machines, Support Vector Machines, and Deep Neural Networks are applied to evaluate predictive performance. Cross-validation and ensemble learning are employed to enhance robustness and mitigate overfitting. Furthermore, interpretability is addressed through feature importance analysis and SHAP values, enabling the identification of key determinants influencing startup outcomes. Experimental results demonstrate that hybrid and ensemble models outperform individual classifiers in terms of accuracy, precision, and recall. The proposed framework provides actionable insights for entrepreneurs, venture capitalists, and policymakers, supporting data-driven decision-making and resource optimization within the entrepreneurial ecosystem.