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Predictive analytics for rice production and yields through machine learning

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dc.contributor.advisorEstira, Marydel C.
dc.contributor.authorRamos, Mark Angelo V.
dc.contributor.authorEstacio, Jowill Dave B.
dc.contributor.authorIndong, Raven Icy C.
dc.contributor.authorPartible, Anacel C.
dc.contributor.chairSardeng, Shekainah Kim, A.
dc.contributor.committeememberMalicdem, Alvin R.
dc.date.accessioned2026-03-13T07:15:01Z
dc.date.available2026-03-13T07:15:01Z
dc.date.issued2024-12
dc.description.abstractThis study aimed to predict rice production and yields in the Province of La Union using a dataset collected from Department of Agriculture Regional Office I. The research employed the CRISP-DM methodology, utilizing machine learning algorithms, focusing on the Random Forest model due to itshigh accuracy and robustness. The dataset included key variablessuch as year, land area, types of seeds, water source, seasons, and municipalities, which were analyzed and processed to train the model. The structured approach of CRISP-DM ensured a comprehensive analysis through its phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Preliminary results demonstrated that the Random Forest model achieved excellent accuracy in predicting rice yields, providing valuable insights for agricultural stakeholders. These insights assisted in strategic agricultural planning, resource allocation, and sustainable farming practices, emphasizing the potential of machine learning in enhancing food security.
dc.identifier.citationRamos, M. A. V., Estacio, J. D. B., Indong, R. I. C., & Partible, A. C. (2024) [Unpublished Undergraduate Feasibility Study]. Don Mariano Marcos Memorial State University – Mid La Union Campus. Lakasa ti Sirib, DMMMMSU Institutional Repository.
dc.identifier.urihttps://lakasa.dmmmsu.edu.ph/handle/123456789/1199
dc.language.isoen_US
dc.publisherDon Mariano Marcos Memorial State University – Mid La Union Campus
dc.sdgSDG 12
dc.titlePredictive analytics for rice production and yields through machine learning
dc.typeThesis
dspace.entity.typePublication
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