CREATION OF PREDICTIVE MACHINE LEARNING MODELS THAT SUPPORT STARTUP EVALUATION, INNOVATION GROWTH, AND ENTREPRENEURSHIP IN THE U.S. ECONOMY

Fatima Al-Hassan; Salman Al-Farisi; Laila Al-Mutairi; Omar Al-Qahtani

doi:10.61796/jgrpd.v1i12.1728

Authors

Fatima Al-Hassan
fatimaa@gmail.com
Department of Computer Science, King Saud University, Saudi Arabia
Salman Al-Farisi Department of Computer Science, King Saud University, Saudi Arabia
Laila Al-Mutairi Department of Computer Science, King Saud University, Saudi Arabia
Omar Al-Qahtani Department of Computer Science, King Saud University, Saudi Arabia

Vol. 1 No. 12 (2024): Journal of Geography, Regional Planning and Development

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December 28, 2024

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Objective: Startup success prediction is crucial for venture capital investment decisions and entrepreneurial ecosystem development. This paper presents predictive machine learning models designed to evaluate startup potential, forecast innovation growth, and support entrepreneurship in the U.S. economy. Method: Our framework integrates survival analysis, network analysis, and natural language processing to assess startup viability across multiple dimensions including team composition, market opportunity, and product innovation. The models are trained on comprehensive datasets encompassing startup characteristics, funding histories, and outcomes. Results: Evaluation results demonstrate 84% accuracy in predicting startup success within three years, with feature importance analysis revealing team experience and market timing as critical success factors. Novelty: The research contributes to entrepreneurial finance literature and provides practical tools for investors and policymakers.

Al-Hassan, F., Al-Farisi, S., Al-Mutairi, L., & Al-Qahtani, O. (2024). CREATION OF PREDICTIVE MACHINE LEARNING MODELS THAT SUPPORT STARTUP EVALUATION, INNOVATION GROWTH, AND ENTREPRENEURSHIP IN THE U.S. ECONOMY. Journal of Geography, Regional Planning and Development, 1(12), 8–14. https://doi.org/10.61796/jgrpd.v1i12.1728

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