AN EFFICIENT NOVEL APPROACH FOR START-UP SUCCESS RATE PREDICTIONS USING ML PARADIGMS

Abstract

The proposed study titled “An Efficient Novel Approach for Start-up Success Rate Predictions Using ML Paradigms” focuses on developing an intelligent predictive system that evaluates the likelihood of success for start-ups using advanced Machine Learning (ML) techniques. In today’s competitive entrepreneurial ecosystem, start-ups face high failure rates due to factors such as poor market analysis, inadequate funding strategies, and ineffective business planning. Traditional evaluation methods often rely on subjective judgment and limited data, which may not accurately predict future outcomes. This research aims to address these limitations by leveraging data-driven approaches to provide objective and reliable predictions. The proposed methodology involves collecting and analyzing diverse datasets that include factors such as financial performance, funding history, market trends, founder experience, product innovation, and customer engagement. The data is preprocessed using techniques such as normalization, missing value handling, and feature selection to ensure quality and relevance. Multiple machine learning models, including Logistic Regression, Random Forest, Support Vector Machine (SVM), and Gradient Boosting, are applied to classify start-ups into successful or unsuccessful categories. Additionally, ensemble learning techniques are employed to combine the strengths of individual models, thereby improving prediction accuracy and robustness. Experimental results demonstrate that the proposed system achieves high accuracy and reliability in predicting start-up success rates. The model effectively identifies key factors influencing success, providing valuable insights for investors, entrepreneurs, and policymakers. The system can be integrated into decision support platforms to assist in investment evaluation and strategic planning. In conclusion, this research presents a scalable and efficient solution for predicting start-up success using machine learning paradigms. By combining data analytics and intelligent modeling, the system enhances decision-making processes and supports the growth of sustainable entrepreneurial ventures.