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Published in:

Volume 7 Issue 6
June-2020
eISSN: 2349-5162

Unique Identifier

JETIR2006477

Page Number

1005-1009

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Title

A Supervised Learning Approach for the Prediction of Prices in different Agricultural Commodities

ISSN

2349-5162

Cite This Article

"A Supervised Learning Approach for the Prediction of Prices in different Agricultural Commodities", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 6, page no.1005-1009, June-2020, Available :http://www.jetir.org/papers/JETIR2006477.pdf

Abstract

The objective of this paper is to to provide real time application which is an efficient tool for price prediction.This work is based on finding suitable data models that help in achieving high accuracy and generality of price prediction. Using Supervised learning technique, the knn algorithm predicts the future price for the crop using some of the parameters such as climate factor, supply details, demands and government policies.

Key Words

Machine Learning, Supervised Learning Technique, Crop price, Climate factor, Supply details and Demand.

Cite This Article

"A Supervised Learning Approach for the Prediction of Prices in different Agricultural Commodities", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 6, page no. pp1005-1009, June-2020, Available at : http://www.jetir.org/papers/JETIR2006477.pdf

Publication Details

Published Paper ID: JETIR2006477
Registration ID: 234674
Published In: Volume 7 | Issue 6 | Year June-2020
DOI (Digital Object Identifier): http://doi.one/10.1729/Journal.23979
Page No: 1005-1009
ISSN Number: 2349-5162

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Cite This Article

"A Supervised Learning Approach for the Prediction of Prices in different Agricultural Commodities", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 6, page no. pp1005-1009, June-2020, Available at : http://www.jetir.org/papers/JETIR2006477.pdf




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