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Crop price data interpretation: A comparison of machine learning

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dc.contributor.author Hirpara, Jignesh
dc.contributor.author Vanjara, Pratik
dc.date.accessioned 2023-05-16T05:25:41Z
dc.date.available 2023-05-16T05:25:41Z
dc.date.issued 2022-01
dc.identifier.citation Hirpara,J.& Vanjara,P.(2022).Crop price data interpretation: A comparison of machine learning.International Multidisciplinary journal of applied research,1(6),20-30. en_US
dc.identifier.issn 2321-7073
dc.identifier.uri http://10.9.150.37:8080/dspace//handle/atmiyauni/963
dc.description.abstract Machine learning and its methodologies are used in agribusiness domains to predict edit costs based on stock availability and generation. On a daily basis, a massive amount of data is generated through the display of farming products. Horticulture has a large amount of data, but unfortunately, much of it isn't able to find out inconspicuous details in information. Edit cost estimates are more beneficial to agriculturists and the agriculture society since they demand proper timing. Information mining procedures that have progressed play a critical role in the discovery of hidden design in data. Following Designs, Cluster Analysis, and visualization methodologies are used to provide a unique representation to predict the horticultural edit cost. Past trim cost, climate, current advertise cost, stock accessibility, and up and coming trim generation in current year or season are all used to compare information mining procedure execution.Recently, the most often used programmer has been designed for cost inquiry rather than cost determination. When compared to individual agriculturists in various countries with stable environments, India's agribusiness generation is exceptionally instable, and without appropriate MSP, it will not benefit agriculturists and farming crew. If ranchers and agribusiness personnel are given the opportunity to appropriate alter costs, destitution in India can be reduced. en_US
dc.language.iso en en_US
dc.publisher International Multidisciplinary journal of applied research en_US
dc.subject DATA MINING en_US
dc.subject MINIMUM SUPPORT PRICE en_US
dc.subject MACHINE LEARNING en_US
dc.subject AGRICULTURAL en_US
dc.subject FARMING FRATERNITY en_US
dc.subject AGRIBUSINESS FRATERNITY en_US
dc.subject CROP PRICE en_US
dc.subject MSP en_US
dc.title Crop price data interpretation: A comparison of machine learning en_US
dc.type Article en_US


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