Constructing Model for Forecasting the Production using the Classification Technique in Data Mining for the District of Tamil Nadu
M.C.S. Geetha

Ms. M.C.S. Geetha, Assistant Professor, Department of Computer Applications, Kumaraguru College of Technology, Coimbatore (Tamil Nadu), India. 

Manuscript received on 09 September 2019 | Revised Manuscript received on 18 September 2019 | Manuscript Published on 11 October 2019 | PP: 164-168 | Volume-8 Issue-11S September 2019 | Retrieval Number: K103509811S19/2019©BEIESP | DOI: 10.35940/ijitee.K1035.09811S19

Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (

Abstract: Data mining plays an essential role in the cropproduction. It is a major field for forecasting and analyzing the crop. The vital role of the cultivator is to know about the production of the crop. In the years before, forecasting was carried out by taking into account the cultivator’s previous experience on the selected area. The forecasting was the important criteria which should be solved by considering the data on hand. By using Data mining method, the enhanced selection can be done. Various Data Mining methods have been used for calculating the upcoming year’s production. This investigation helps to recommend a model for forecasting the yield from the earlier data. For accomplishing and forecasting the yield association rule mining in data mining has been used. This helps to focus on implementing a system which may be used for forecasting the yield in the upcoming years. This research aims at presenting a detailed study by forecasting the yield using association rules in data mining technique for the chosen area in India. The results have shown that the anticipated work done is working well in order to predict the production of the yield.

Keywords: Association Rule Mining, Data Mining, Agriculture, Forecasting Production.
Scope of the Article: Data Mining