Abstract: Farmers play a very important role in the agriculture sector. When the prices fall after the harvesting of the crops, farmers face massive amounts of losses because this country's GDP is affected by the price fluctuations of agricultural products. Crop price evaluation and estimation are done to take an intelligent decision before farming a specific type of seasonal crops. Predicting the price of a crop will help in making better decisions which results in minimizing the losses and managing the risk of price fluctuations. In this research paper, we had predicted the price of different crops by analysing the previous rainfall and Wholesale Price Index (WPI) data. We used the Decision Tree Regressor and Random Forest, a Supervised machine learning algorithm to analyse the previous data and Time Series Analysis to estimate the crop price for upcoming twelve months.

Keywords: Decision Tree Regressor, Random Forest, Machine Learning, Time Series Forecasting

| DOI: 10.17148/IARJSET.2022.95107