Application of Machine Learning Based Predictive Algorithms: A Comprehensive Review

Bolanle, Oluwapamilerin (2023) Application of Machine Learning Based Predictive Algorithms: A Comprehensive Review. Journal of Engineering, Mechanics and Modern Architecture, 2 (3). pp. 45-55. ISSN 2181-4384

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Abstract

Machine learning-based predictive algorithms have emerged as powerful tools for extracting insights and making predictions from complex datasets across diverse domains. This comprehensive review examines the state-of-the-art in applying machine learning for predictive analytics, covering key algorithms, application areas, and recent advances. We provide an overview of popular supervised and unsupervised learning techniques used for prediction tasks, including regression, classification, clustering, and dimensionality reduction. The review explores applications of predictive machine learning in fields such as healthcare, finance, manufacturing, and transportation. We analyze how machine learning models are being used to forecast disease progression, detect financial fraud, predict equipment failures, optimize supply chains, and enable autonomous navigation. The paper also discusses important considerations in developing predictive models, including feature engineering, model selection, hyperparameter tuning, and performance evaluation. Additionally, we examine emerging trends like automated machine learning, interpretable AI, and edge computing for real-time predictions. Key challenges such as data quality, model interpretability, and ethical concerns are highlighted. The review concludes by identifying promising research directions to further advance the capabilities and real-world impact of machine learning-based predictive analytics. This comprehensive survey provides researchers and practitioners with an up-to-date perspective on the current landscape and future potential of predictive machine learning across industries.

Item Type:	Article
Subjects:	Q Science > Q Science (General)
Divisions:	Postgraduate > Master's of Islamic Education
Depositing User:	Journal Editor
Date Deposited:	07 Aug 2024 10:32
Last Modified:	10 Aug 2024 12:00
URI:	http://eprints.umsida.ac.id/id/eprint/13966

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