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Data Mining, Machine Learning, and Statistical Modeling for Predictive Analytics with Behavioral Big Data.

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Title: Data Mining, Machine Learning, and Statistical Modeling for Predictive Analytics with Behavioral Big Data.
Authors: ARUNKUMAR, M.1, RAJKUMAR, K.1, SALEM JEYASEELAN, W. R.2, NATRAJ, N. A.3 natraj@sidtm.edu.in
Source: Technical Gazette / Tehnički Vjesnik. 2025, Vol. 32 Issue 1, p72-77. 6p.
Subjects: Data mining, Data analytics, Machine learning, Consumer behavior, Business analytics, Big data
Abstract: This research delves into the transformative impact of the widespread adoption of big data and advancements in predictive analytics on decision-making processes across industries. The study specifically concentrates on the paradigm of behavioral big data computation, integrating a spectrum of data sources, including social media, online platforms, and IoT devices. Employing a comprehensive analysis involving data mining, machine learning, and statistical modeling, the research unveils intricate patterns and insights within the data. The methodology aims to extract meaningful behavioral indicators that significantly influence the outcomes of predictive analytics. Additionally, the study explores how behavioral big data computation impacts the accuracy, timeliness, and reliability of predictive models. Embracing a systematic and in-depth approach, the research aims to provide a thorough understanding of the potential applications and challenges associated with harnessing behavioral big data computation for predictive analytics. Anticipated outcomes encompass insights into the development of robust predictive models capable of anticipating trends, consumer behavior, and market dynamics. This, in turn, empowers organizations to make well-informed strategic decisions in today's dynamic and competitive business landscape. The findings of this research are poised to contribute valuable knowledge, enhancing the efficacy of predictive analytics in diverse business scenarios. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:This research delves into the transformative impact of the widespread adoption of big data and advancements in predictive analytics on decision-making processes across industries. The study specifically concentrates on the paradigm of behavioral big data computation, integrating a spectrum of data sources, including social media, online platforms, and IoT devices. Employing a comprehensive analysis involving data mining, machine learning, and statistical modeling, the research unveils intricate patterns and insights within the data. The methodology aims to extract meaningful behavioral indicators that significantly influence the outcomes of predictive analytics. Additionally, the study explores how behavioral big data computation impacts the accuracy, timeliness, and reliability of predictive models. Embracing a systematic and in-depth approach, the research aims to provide a thorough understanding of the potential applications and challenges associated with harnessing behavioral big data computation for predictive analytics. Anticipated outcomes encompass insights into the development of robust predictive models capable of anticipating trends, consumer behavior, and market dynamics. This, in turn, empowers organizations to make well-informed strategic decisions in today's dynamic and competitive business landscape. The findings of this research are poised to contribute valuable knowledge, enhancing the efficacy of predictive analytics in diverse business scenarios. [ABSTRACT FROM AUTHOR]
ISSN:13303651
DOI:10.17559/TV-20231102001073