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A model to analyze interpret activities of agriculture fraternity with data interpretation

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dc.contributor.author Hirpara, Jignesh
dc.contributor.author Vanjara, Pratik
dc.date.accessioned 2023-05-16T03:26:14Z
dc.date.available 2023-05-16T03:26:14Z
dc.date.issued 2021
dc.identifier.citation Hirpara,J.&Vanjara,P.(2021).A model to analyze interpret activities of agriculture fraternity with data interpretation.Kala Sarovar(UGC Care Group-1 Journal) ,24(1),22-26. en_US
dc.identifier.issn 0975-4520
dc.identifier.uri http://10.9.150.37:8080/dspace//handle/atmiyauni/953
dc.description.abstract In Agriculture, field farmers and agriculture, fraternity has to take impotent decisions every day. In our country, farmers are not getting the expected price from their crops. Crop price mostly depends on the market and the weather. Any farmer is enthused about knowing how a great deal of crop price he will foresee. Previously, the yield figure was performed by contemplating a farmer's understanding of the explicit field and collect. The crop price prediction is a serious problem that remains to be resolved based on statistical data. Tracking patterns, visualization and other techniques of data mining is the best option for solving this problem. This proposal implements a system to predict crop prices based on the last 10-year data. This is done through the use of various data mining techniques. This research focuses on future crop price prediction base on every dependent factor. If we have the previous year data available in which corresponding crop prices are recorded and this recoded price will help to classify crop price. en_US
dc.language.iso en en_US
dc.publisher Kala Sarovar(UGC Care Group-1 Journal) en_US
dc.subject Data Mining, Crop Price en_US
dc.subject regression analysis techniques of Data mining en_US
dc.subject Tracking Patterns of Data mining en_US
dc.subject visualization techniques of Data mining en_US
dc.subject Data Mining techniques en_US
dc.title A model to analyze interpret activities of agriculture fraternity with data interpretation en_US
dc.type Article en_US


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