Comparative Analysis of Machine Learning Algorithms: Performance, Scalability, and Industry-Specific Applications

Akansh Mani, Arshia Mani

Abstract: This study conducts a comparative analysis of popular machine learning algorithms, including Random Forests, Support Vector Machines, Neural Networks, Logistic Regression, K-Means, and DBSCAN, across diverse datasets and domains. The research evaluates performance based on metrics such as accuracy, scalability, robustness, and sensitivity to noise. The findings highlight that supervised learning algorithms excel in structured datasets, particularly in healthcare and finance, while unsupervised methods demonstrate superior scalability for large, unstructured datasets in domains like e-commerce. These results underscore the importance of aligning algorithm selection with industry-specific requirements to optimize performance and outcomes.

Keywords: Machine learning algorithms, supervised learning, unsupervised learning, industry applications, performance metrics

How to Cite?: Akansh Mani, Arshia Mani, "Comparative Analysis of Machine Learning Algorithms: Performance, Scalability, and Industry-Specific Applications", Volume 13 Issue 12, December 2024, International Journal of Science and Research (IJSR), Pages: 637-641, https://www.ijsr.net/getabstract.php?paperid=SR241130025514, DOI: https://dx.doi.org/10.21275/SR241130025514