Agriculture Commodity Price Forecasting using Ml Techniques
Varun R1, Neema N.2, Sahana H. P.3, Sathvik A.4, Mohammed Muddasir5
1Varun R, Department of Information Science and Engineering, VVCE, Mysuru (Karnataka), India.
2Neema N, Department of Information Science and Engineering, VVCE, Mysuru (Karnataka), India.
3Sahana H P, Department of Information Science and Engineering, VVCE, Mysuru (Karnataka), India.
4Sathvik A, Department of Information Science and Engineering, VVCE, Mysuru (Karnataka), India.
5Mohammed Muddasir, Department of Information Science and Engineering, VVCE, Mysuru (Karnataka), India.
Manuscript received on 09 December 2019 | Revised Manuscript received on 17 December 2019 | Manuscript Published on 31 December 2019 | PP: 729-732 | Volume-9 Issue-2S December 2019 | Retrieval Number: B12261292S19/2019©BEIESP | DOI: 10.35940/ijitee.B1226.1292S19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: India is mainly an agricultural country the farmer is an important part of agriculture. Agriculture mainly depends on him. Even then the farmers cannot predict prices for their commodities because prediction of prices plays a major challenge. Several characteristics are taken into account so that the crop price forecast is accurate. We consider the attributes of the Mysore region to make it a real-time application framework. Price prediction is a big issue for farmers who are not aware of the market prices. Forecasting price of agriculture commodities helps the agriculturist and also the agriculture department of mysore region to make decisions. The new model predicts the accuracy for the agricultural yields and it also avoids the role of middle man.
Keywords: Price Prediction, Data Mining, Naïve Baysian Classifier, k-means, Artificial Neural Networks, Support Vector Machine, Prediction, Extended Kalman filter, Wavelet, Error Analysis.
Scope of the Article: Data Mining