The Crop Price Prediction Using Machine Learning: Preliminary Stage

The objective of our research is mostly concerned with agriculture. Farmers are the key players in agriculture. Knowing how much a crop will cost will enable you to make smarter judgments, which will reduce thelosses and lower the risk of price changes. An ML model that forecasts agricultural prices in advance while accurately analyzingthe crop may be able to solve this issue. A predictive system, a statistical method combiningmachine learning and data collecting, is used in many applications, including healthcare, retail, education, and government sectors. Its usage in the agriculture sector has comparable relevance. The back-endpredictive model for this project is created utilizingmachine learning algorithms. The steps involved in creating a predictive model are data collecting, data cleaning, data mining, and validation. The goal is to give farmers an intuitive user interface, and this model should correctly forecast crop market value given the real-time variables provided.