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

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.