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 Book Chapter Book Chapter Faculty Publications- Book Chapter 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 Comparative Analysis of Different Machine Learning Prediction Models for Seasonal Rainfall and Crop Production in Cultivation Subject The topic of the resource Crop prediction; Linear model; Machine learning; Rain fall; Regression model Description An account of the resource Agriculture is one of the strengths of India, from the last few years, gradually the agriculture growth is going downwards in other side the population growth is upwards. Reason for agricultural downward growth depends on so many parameters. The rainfall is one of the main parameters which affects the crop yield. Because of this, the farmers are also facing the loss. If they know this information in prior, the farmers can plan accordingly the type of crop suited for the particular season and it helps the farmer to get good profit out of it. Machine learning scientific and statistical methods are used for predicting the rain fall and crop yield. Kharif and Rabi are two seasons taken for analysis. The regressor predicting models are constructed to predict the seasonal rainfall and crop yield. This study primarily focuses on seasonal crop production prediction, which is dependent on rainfall. The different types of machine learning regression method are used to achieve better results. The performance of comparison models is evaluated using different metrics. Finally, the linear regression and Bayesian linear regression models comparatively produce the best result in terms of accuracy for rainfall prediction. The boosted decision tree regression model is achieving the better result for crop prediction. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. Creator An entity primarily responsible for making the resource Deepa S.; Gokila S. Source A related resource from which the described resource is derived Lecture Notes on Data Engineering and Communications Technologies, Vol-139, pp. 277-289. Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH 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/10.1007/978-981-19-3015-7_20" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-19-3015-7_20</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138159983&amp;doi=10.1007%2F978-981-19-3015-7_20&amp;partnerID=40&amp;md5=9c4e9c76643c050c57e4bfa4268b3066" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138159983&amp;doi=10.1007%2f978-981-19-3015-7_20&amp;partnerID=40&amp;md5=9c4e9c76643c050c57e4bfa4268b3066</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23674512 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 Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Deepa S., School of Sciences, CHRIST (Deemed to be University), Bengaluru, India; Gokila S., Department of Computer Applications, Hindustan Institute of Technology and Science, Chennai, India