Construction of Entrepreneurial Project Recommendation System Based on Adaboost Combined Classification Algorithm

Abstract

This study presents the development and evaluation of an entrepreneurial project recommendation system based on the AdaBoost combined classification algorithm. In the rapidly evolving landscape of entrepreneurship, the ability to efficiently identify and pursue viable venture opportunities is crucial for success. Traditional methods of project selection often rely on intuition and historical trends, which may be subjective or outdated. To address this challenge, recommendation systems powered by advanced machine learning algorithms offer data-driven guidance and decision support. The entrepreneurial project recommendation system proposed in this study leverages the AdaBoost algorithm, renowned for improving classification accuracy and handling complex datasets. Through meticulous experimental design and rigorous analysis, the system demonstrates strong performance in providing accurate and relevant recommendations to users. Evaluation metrics including accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC-ROC) indicate the system's efficacy in discriminating between promising and non-promising projects. The findings of this study have significant implications for entrepreneurs, investors, and decision-makers, empowering them to make informed decisions and mitigate risks in venture investments. Future research endeavours could explore additional data sources, personalized algorithms, and longitudinal studies to further enhance the system's effectiveness and applicability in dynamic entrepreneurial environments. Overall, the entrepreneurial project recommendation system offers a valuable resource for navigating the complexities of venture investments and capitalizing on emerging opportunities in entrepreneurship.