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September 28, 2025Open Access

A Framework to Predicting Startup Success Growth With Multiple Algorithms

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Authors

KRKandadi Thirupathi Reddy

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Overview

Novel framework employs machine learning algorithms to enhance predictive performance of startups, indicating key success factors.

Key Points

  • Hybrid and ensemble models significantly improve predictive performance in startup growth outcomes, achieving higher accuracy and precision.
  • Experimental results highlight improved capabilities of multiple machine learning algorithms like Random Forest and Deep Neural Networks.
  • The framework uses diverse features including financial indicators and team characteristics to forecast startup success effectively.
  • Robust validation methods like cross-validation mitigate overfitting, ensuring the model's reliability and interpretability for stakeholders.

Cite This Study

Kandadi Thirupathi Reddy (2025) studied this question.

synapsesocial.com/papers/68d913a34ddcf71ba560b88chttps://doi.org/10.36227/techrxiv.175751434.42657764/v2
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