Advancements in Machine Learning Algorithms for Big Data Analytics

This study investigates recent advancements in machine learning (ML) algorithms for Big Data analytics, with a focus on scalability, real-time processing, and ethical considerations. A qualitative literature review was performed, examining recent ML developments through thematic analysis of peer-reviewed publications and industry reports. The findings highlight notable improvements in scalability via distributed computing frameworks such as Apache Spark and Hadoop, as well as enhanced real-time processing achieved through online learning techniques. Nevertheless, challenges persist in maintaining model accuracy in the presence of noisy data and mitigating algorithmic bias. Ethical issues concerning fairness, transparency, and accountability were also identified. This research advances understanding of ML's role in Big Data applications and provides practical insights for deploying scalable, interpretable, and ethically responsible models across industries. Future work should focus on refining hybrid approaches and evaluating their applicability in real-world scenarios.