Predicting the Poverty Alleviation in the Province of Eastern Samar using Data Mining Techniques
AJared Harem Q. Celis1, Andres C. Pagatpatan, Jr.2
1Jared Harem Q. Celis, Information Technology Department, Eastern Samar State University – Guiuan Campus, Guiuan, Philippines.
2Dr. Andres C. Pagatpatan, Jr., Administration, Eastern Samar State University – Guiuan Campus, Guiuan, Philippines.
Manuscript received on 07 August 2019. | Revised Manuscript received on 15 August 2019. | Manuscript published on 30 September 2019. | PP: 7140-7145 | Volume-8 Issue-3 September 2019 | Retrieval Number: C6082098319/2019©BEIESP | DOI: 10.35940/ijrte.C6082.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Poverty has been a main concern for century in any part of the world. The abrupt increase of population in the country and the inevitable rise of the inflation rate due to the economic challenges and other factors, it is clearly manifested that poverty is a problem that needs to be addressed seriously. With the available various advanced-technology nowadays, this problem on poverty maybe reduced with the aide of Data Mining which is a part of Data Science. This paper focused on predicting the poverty alleviation using Data Mining techniques based from all available data from the Philippine Statistic’s Authority, National Economic Development Authority, and Department of Social Welfare and Development. The application of supervised learning in Data Mining specifically, NaiveBayes Algorithm, Decision Tree J48 Algorithm, and K- Nearest Neighbour Algorithm has been utilized for the prediction of poverty alleviation in the province of Eastern Samar. The results of this study unveil that among the core indicators in identifying poverty, it is the “Economic Sector” with the attribute “Income” is the most significant factor that affects poverty alleviation in the province.
Keywords: Classification Model, Data Mining, Poverty Alleviation, Poverty Incidence, Prediction.
Scope of the Article: Classification