Exploring Unconventional Sources in Big Data: A Data Lifecycle Approach for Social and Economic Analysis with Machine Learning

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Published: Oct 7, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i11s.8187
Keywords:
Unconventional data sources, Big Data, Data Lifecycle Approach, social analysis, economic analysis, linear regression, random forest, XGBoost, data collection

Main Article Content

Mahadevi Namose
Research Scholar, Department of Computer Science and Engineering, Sardar Patel University, Bhopal, MP, India
Tryambak Hiwarkar
Professor, Department of Computer Science and Engineering, Sardar Patel University, Bhopal, MP, India

Abstract

This study delves into the realm of leveraging unconventional sources within the domain of Big Data for conducting insightful social and economic analyses. Employing a Data Lifecycle Approach, the research focuses on harnessing the potential of linear regression, random forest, and XGBoost techniques to extract meaningful insights from unconventional data sources. The study encompasses a structured methodology involving data collection, preprocessing, feature engineering, model selection, and iterative refinement. By applying these techniques to diverse datasets, encompassing sources like social media content, sensor data, and satellite imagery, the study aims to provide a comprehensive understanding of social and economic trends. The results obtained through these methods contribute to an enhanced comprehension of the intricate relationships within societal and economic systems, further highlighting the importance of unconventional data sources in driving valuable insights for decision-makers and researchers alike.

Article Details

How to Cite
Namose, M. ., & Hiwarkar, T. . (2023). Exploring Unconventional Sources in Big Data: A Data Lifecycle Approach for Social and Economic Analysis with Machine Learning. International Journal on Recent and Innovation Trends in Computing and Communication, 11(11s), 566–579. https://doi.org/10.17762/ijritcc.v11i11s.8187
Issue
Vol. 11 No. 11s (2023)
Section
Articles

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