Predictive Analytics using Machine Learning in Big Data Environments
DOI:
https://doi.org/10.15680/IJCTECE.2019.0203003Keywords:
Predictive Analytics, Machine Learning, Big Data, Forecasting, Data Quality, Scalability, Ethical Considerations, Data Integration, Algorithmic Bias, Real-time AnalyticsAbstract
Predictive analytics, powered by machine learning (ML), has become a cornerstone in extracting actionable insights from vast datasets in big data environments. By leveraging historical data, ML algorithms can forecast future trends, behaviors, and outcomes, enabling proactive decision-making across various sectors. This paper delves into the tools, methodologies, and challenges associated with implementing predictive analytics in big data contexts. We explore the evolution of ML algorithms, the integration of big data technologies, and the emerging trends shaping the future of predictive analytics. Additionally, we address the challenges organizations face, including data quality, scalability, and ethical considerations, offering insights into overcoming these obstacles. Through a comprehensive review, this paper aims to provide a holistic understanding of predictive analytics in the realm of big data.
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