Machine Learning for Data Analysis in Research: A Review
Keywords:
Machine Learning, Data Analysis, Deep Learning, Internet of Things (IoT), Data Processing, Decision-MakingAbstract
The implementation of machine learning (ML) in data analytics has drawn significant attention in recent years due to its potential to revolutionise a variety of domains. Reviewing the varied literature on the use of machine learning in research data analysis is the aim of this essay. This review underscores the growing necessity of integrating machine learning (ML) techniques with big data (BD) analytics to address challenges in scalability, speed, and complexity. ML offers promising tools for generating valuable insights and driving smarter decision-making in various domains. However, to fully harness its potential, future research must focus on developing adaptable, cost-effective, and robust ML models capable of handling noisy, incomplete, and real-time data. As data volumes grow, the synergy between ML and BD will remain vital in advancing analytical capabilities, particularly for Internet of Things (IoT) applications and business intelligence.
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