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 |