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] |
| Copyright of Technical Gazette / Tehnički Vjesnik is the property of Tehnicki Vjesnik and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 182371830 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Data Mining, Machine Learning, and Statistical Modeling for Predictive Analytics with Behavioral Big Data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22ARUNKUMAR%2C+M%2E%22">ARUNKUMAR, M.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22RAJKUMAR%2C+K%2E%22">RAJKUMAR, K.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22SALEM+JEYASEELAN%2C+W%2E+R%2E%22">SALEM JEYASEELAN, W. R.</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22NATRAJ%2C+N%2E+A%2E%22">NATRAJ, N. A.</searchLink><relatesTo>3</relatesTo><i> natraj@sidtm.edu.in</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Technical+Gazette+%2F+Tehnički+Vjesnik%22">Technical Gazette / Tehnički Vjesnik</searchLink>. 2025, Vol. 32 Issue 1, p72-77. 6p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analytics%22">Data analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Consumer+behavior%22">Consumer behavior</searchLink><br /><searchLink fieldCode="DE" term="%22Business+analytics%22">Business analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Big+data%22">Big data</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Technical Gazette / Tehnički Vjesnik is the property of Tehnicki Vjesnik and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=182371830 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.17559/TV-20231102001073 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 6 StartPage: 72 Subjects: – SubjectFull: Data mining Type: general – SubjectFull: Data analytics Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Consumer behavior Type: general – SubjectFull: Business analytics Type: general – SubjectFull: Big data Type: general Titles: – TitleFull: Data Mining, Machine Learning, and Statistical Modeling for Predictive Analytics with Behavioral Big Data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: ARUNKUMAR, M. – PersonEntity: Name: NameFull: RAJKUMAR, K. – PersonEntity: Name: NameFull: SALEM JEYASEELAN, W. R. – PersonEntity: Name: NameFull: NATRAJ, N. A. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: 2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 13303651 Numbering: – Type: volume Value: 32 – Type: issue Value: 1 Titles: – TitleFull: Technical Gazette / Tehnički Vjesnik Type: main |
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