A Performance Assessment on Unsupervised Machine Learning Models at GDP Datasets

Rathindra Nath Mohalder, Bijoy Podder and Mohammad Habibur Rahaman
Additional contact information
Rathindra Nath Mohalder: Department of Computer Science and Engineering, Khulna Khan Bahadur Ahsanullah University, Bangladesh
Bijoy Podder: Department of Computer Science and Engineering, Khulna Khan Bahadur Ahsanullah University, Bangladesh
Mohammad Habibur Rahaman: Department of Computer Science and Engineering, Khulna Khan Bahadur Ahsanullah University, Bangladesh

International Journal of Research and Innovation in Social Science, 2025, vol. 9, issue 6, 627-639

Abstract: Machine learning (ML) with unsupervised ML (UML) is a core engine for economic development across regions. UML enables novel solutions, improves efficiency, and boosts economic progress by letting advanced algorithms analyze large datasets with no labeled inputs. UML could have a substantial effect on the Gross Domestic Product. However, none of these speculative events might occur without overcoming severe challenges, including a shortage of skills to use it, ethical issues (see No Encoded Morality), data privacy problems, and alarmingly inequitable access to technology. By emphasizing UML development and adoption, countries will be able to move past these hurdles & they can wield their power, promoting a sustainable economy worldwide. Focusing on the unsupervised learning aspect, this work could further expand on how such breaks away from or augments supervised algorithms in similar applications. Using UML on GDP datasets helps to gain valuable insights and trends and facilitates data-driven decisions to enhance economic planning & policymaking. Some unique applications that are not possible with supervised approaches based on training from label data to input data, e.g., clustering, anomaly detection, and dimension reduction, have also been implemented by UML analyzing the GDP-related data without any label. UML requires no labeled data for training and is evaluated based on the performance of such models in extracting features, acting upon clusters, or reducing input space dimensions. UML Models are leveraged in multiple domains to gain insights from unlabeled data, make decisions, and optimize processes by using pattern discovery & data insights, anomaly detection, and flexibility across domains, further improving supervised learning. Unsupervised UML models find their way into an eclectic amalgamation of domains through which they reshape data-driven decision-making and operational workflows, sometimes even innovation. UML can transform, but it is not always used correctly. So, ethics and legality need to be accounted for, more so given social benefits through GDP.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.rsisinternational.org/journals/ijriss/ ... -issue-6/627-639.pdf (application/pdf)
https://rsisinternational.org/journals/ijriss/arti ... els-at-gdp-datasets/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bcp:journl:v:9:y:2025:issue-6:p:627-639

Access Statistics for this article

International Journal of Research and Innovation in Social Science is currently edited by Dr. Nidhi Malhan

More articles in International Journal of Research and Innovation in Social Science from International Journal of Research and Innovation in Social Science (IJRISS)
Bibliographic data for series maintained by Dr. Pawan Verma ().