Predictive Opportunity Analysis (POA): A Machine Learning Framework for Techpreneurial Foresight and Innovation Strategy

Abstract

This study introduces the Predictive Opportunity Analysis (POA) framework, an advanced, datadriven system designed to quantify and interpret opportunity potential within the technology entrepreneurship ecosystem. The persistent challenge of uncertainty in techpreneurial opportunity evaluation necessitates a shift from intuitive judgments to transparent, evidence-based foresight. This paper details the development and empirical validation of a multi-component predictive architecture that estimates startup success probabilities. The framework integrates a suite of ensemble machine learning algorithms—including Random Forest, XGBoost, LightGBM, and a meta-model—optimised with Optuna-based hyperparameter tuning. The analysis utilises the Kaggle Startup Success Prediction dataset to evaluate predictive accuracy and interpret feature significance through SHAP (SHapley Additive exPlanations) explainability. The results demonstrate the framework’s strong predictive capacity within the scope of this dataset, with the LightGBM model achieving the highest performance, yielding a ROC-AUC of 0.8331 and an accuracy of 78.38%. The explainability analysis reveals that social capital (relationships) is the most influential predictor of startup success in this dataset, followed by financial resources (funding_total_usd) and execution capability (milestones). These findings offer empirical support for the Resource-Based View (RBV) of the firm, highlighting the salience of social capital in the observed venture outcomes. While generalisation beyond the studied context warrants caution, the findings contribute to the emerging field of computational entrepreneurship by offering a transparent, reproducible, and theoretically grounded framework for foresight and opportunity identification, with practical relevance for entrepreneurs, investors, and innovation policymakers. Keywords: