Design and Implementation of Price Forecasting System for Agricultural Commodities using Machine Learning
DOI:
https://doi.org/10.61841/pqdc5x44Keywords:
Machine Learning, Decision Tree Regression, Prediction, Data Set, Annual Rainfall, WPI IndexAbstract
India is an country which has agriculture as it’s backbone. The recent trends in agriculture is extremely devastating and show huge loss in crops. The profit, too is very low. There are various reasons which affect the profit. We take into consideration those various factors so that the crop market prediction is accurate. The system is based upon Machine Learning Techniques. The algorithm used for crop price prediction is Decision Tree Regression technique. It is used to predict the crop value using the data trained from authentic data sets and forecast the price of the commodity for the next year. This implementation proves to be promising with about >70% accuracy rate.
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