Leveraging Machine Learning Algorithms for Predictive Analytics in Big Data: Challenges and Opportunities

This article explores the integration of machine learning with Big Data for predictive analytics, highlighting its potential and challenges. It provides an overview of key machine learning algorithms, such as decision trees, random forests, and neural networks, and discusses their application in Big Data environments. The article examines challenges such as data quality, model interpretability, and ethical concerns surrounding data privacy. Furthermore, emerging technologies like quantum computing and Edge AI are introduced as future trends that could revolutionize predictive analytics. The article also presents case studies from healthcare and finance, showcasing real-world applications of predictive analytics. In conclusion, the article emphasizes the importance of responsible data management and the significant role machine learning will continue to play in driving innovation across industries.

Authors

Publication Details

Journal
Insights in computer, signals and systems.
Published
2024-10-14
DOI
https://doi.org/10.70088/nc8axj56
Primary Topic
Machine Learning and Data Classification
Type
article
Field-Weighted Citation Impact
0.00
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article

Leveraging Machine Learning Algorithms for Predictive Analytics in Big Data: Challenges and Opportunities

Shengyuan Zhang
Insights in computer, signals and systems.
Machine Learning and Data Classification
article

Leveraging Machine Learning Algorithms for Predictive Analytics in Big Data: Challenges and Opportunities

Shengyuan Zhang
article en

Abstract

This article explores the integration of machine learning with Big Data for predictive analytics, highlighting its potential and challenges. It provides an overview of key machine learning algorithms, such as decision trees, random forests, and neural networks, and discusses their application in Big Data environments. The article examines challenges such as data quality, model interpretability, and ethical concerns surrounding data privacy. Furthermore, emerging technologies like quantum computing and Edge AI are introduced as future trends that could revolutionize predictive analytics. The article also presents case studies from healthcare and finance, showcasing real-world applications of predictive analytics. In conclusion, the article emphasizes the importance of responsible data management and the significant role machine learning will continue to play in driving innovation across industries.

Insights in computer, signals and systems.Vol. 1(1)
Industry, innovation and infrastructure
Openalex Percentile: Top 81%
Machine Learning and Data Classification
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Leveraging Machine Learning Algorithms for Predictive Analytics in Big Data: Challenges and Opportunities — Shengyuan Zhang · Insights in computer, signals and systems. (2024) | TGRS Research Map | TGRS