Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A Comparative Study of Machine Learning and Deep Learning Algorithms to Predict Crop Production Subject The topic of the resource Agriculture; Artificial Intelligence; Crop Production; Deep Learning; Label encoder; Machine Learning; R2 score. Description An account of the resource Agriculture is a field that plays an essential part in strengthening a country's economy, especially in agrarian countries like India, where agriculture and crop productivity play a large role in the economy. The research focuses on comparing machine learning and Deep learning algorithms in predicting total crop yield production. The parameters considered for the study are State name, District name, Year, Season, Crop, Area and Production. The dataset is resourced from the data.gov.in website. Random forest from Machine Learning and Sequential model from Deep learning are compared, and the performance metric considered for the study is R2 score. The objective is to assess how well the independent variable predicts the variance in the dependent variable. Random Forest algorithm achieved an R2 score of 0.89, whereas Deep Learning Sequential algorithm gave an R2 score of 0.29. 2023 American Institute of Physics Inc.. All rights reserved. Creator An entity primarily responsible for making the resource Bindu Sri G.; Thomas K.T. Source A related resource from which the described resource is derived AIP Conference Proceedings, Vol-2754, No. 1 Publisher An entity responsible for making the resource available American Institute of Physics Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1063/5.0162300" target="_blank" rel="noreferrer noopener">https://doi.org/10.1063/5.0162300</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176753209&amp;doi=10.1063%2F5.0162300&amp;partnerID=40&amp;md5=db4ea44cad24ebc2aa76db45cd97d81c" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176753209&amp;doi=10.1063%2f5.0162300&amp;partnerID=40&amp;md5=db4ea44cad24ebc2aa76db45cd97d81c</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 0094243X Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Bindu Sri G., Department of Data Science, Christ University, Pune, Maharashtra, India; Thomas K.T., Department of Data Science, Christ University, Pune, Maharashtra, India