Abstract
Our target is focused largely on agriculture. In agriculture, farmers play the most important role. When the price falls after the harvest, farmers face immense losses. A country’s GDP is affected by the price fluctuations of agricultural products. Crop price estimation and evaluation are done to take an intelligent decision before farming a specific type of crop. Predicting the price of a crop will help in taking better decisions which results in minimizing the loss and managing the risk of price fluctuations. In this paper, we predicted the price of different crops by analyzing the previous rainfall and WPI data. We used the decision tree regressor (Supervised machine learning algorithm) to analyze the previous data and predict the price for the latest data and estimate the price for the twelve months to come.
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