Country Cluster Visualization based on Agricultural Imports: Unsupervised Learning Approach

Year : 2024 | Volume : 11 | Issue : 01 | Page : 1-7
By

    R.S. Kamath

  1. P.G. Naik

  2. S.S. Jamsandekar

  1. Associate Professor, Department of Computer Studies, Chhatrapati Shahu Institute of Business Education and Research (CSIBER), Kolhapur, Maharashtra, India
  2. Professor, Department of Computer Studies, Chhatrapati Shahu Institute of Business Education and Research (CSIBER), Kolhapur, Maharashtra, India
  3. Assistant Professor, Department of Computer Studies, Chhatrapati Shahu Institute of Business Education and Research (CSIBER), Kolhapur, Maharashtra, India

Abstract

The clustering algorithm used in this analysis makes it easier for policymakers to understand performance metrics. K-means clustering has been demonstrated to be a tool for analyzing countries’ agricultural imports and a visualization tool for interpreting the results in this study. Authors have used agricultural imports from 190 nations from Knoema, a web-based open data platform. Cereals, meat, and coffee imports from 190 nations in 2016 are included in the dataset. Based on their imports of cereals, meat, and coffee, the nations were divided into three clusters of sizes 24, 13, and 159. According to the findings of this study, 159 nations fall into the cluster category, with average imports of 6.64 million tonnes of cereals, 0.72 million tonnes of meat, and 1.1 million tonnes of coffee, respectively. This shows agrarian prosperity of these nations since the imports are less contrasted with the other two bunches. According to the findings, K-means has the potential to become the most widely used tool for country cluster analysis.

Keywords: K-means, cluster visualization, unsupervised learning, artificial intelligence, cluster designs

[This article belongs to Journal of Artificial Intelligence Research & Advances(joaira)]

How to cite this article: R.S. Kamath, P.G. Naik, S.S. Jamsandekar Country Cluster Visualization based on Agricultural Imports: Unsupervised Learning Approach joaira 2024; 11:1-7
How to cite this URL: R.S. Kamath, P.G. Naik, S.S. Jamsandekar Country Cluster Visualization based on Agricultural Imports: Unsupervised Learning Approach joaira 2024 {cited 2024 Jan 03};11:1-7. Available from: https://journals.stmjournals.com/joaira/article=2024/view=131465

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Regular Issue Subscription Review Article
Volume 11
Issue 01
Received November 17, 2023
Accepted December 2, 2023
Published January 3, 2024