• Sai Dinesh Penagenti
• P.V. Lakshmi

Agriculture and agricultural sectors account for the majority of the country's income and economy in India. Farmers have had a difficult time anticipating agricultural produce prices in the past. Farmers are currently losing a significant amount of money as a result of price volatility induced by climatic change and other price-influencing variables. Farmers are unable to receive the price for their produce that they seek. The purpose of this research is to create a decision-making support model for predicting agricultural commodity prices. This method can be used as a reference for determining what crops a farmer should plant, taking into consideration parameters such as annual rainfall, WPI, and other variables. The system produces a detailed 12-month projection. The decision tree regression technique is a machine learning regression technique.

Price Prediction, Machine Learning, WPI, Decision Tree Regression.

"Agricultural Prices Forecasting Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 6, page no.h853-h859, June-2022, Available :http://www.jetir.org/papers/JETIR2206798.pdf

"Agricultural Prices Forecasting Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 6, page no. pph853-h859, June-2022, Available at : http://www.jetir.org/papers/JETIR2206798.pdf