Crop Price Prediction

Abstract: Accurate crop price prediction is pivotal for optimizing agricultural decision-making and ensuring economic stability for farmers, traders, and policymakers. In this paper, a Random Forest based machine learning framework is proposed to forecast crop price up to 80% accuracy. Advanced feature engineering techniques such as percentile based methods are used to reduce the outlier effects and lag variables to maintain sequence patterns and dependencies. To enhance usability and accessibility, the predictive model is deployed through a Flask-based web application, providing an intuitive user interface for real-time interaction. Total of this implementation combines the methodologies of ML with practical engineering to cope with the versatilities of agriculture commodity price prediction. -- the architecture of the designed system has a great potential for developmental transformation, towards making these systems into data-driven businesses and economic actors based on sustainable, renewable agricultural transformation systems in the larger scope of precision agriculture, economic resilience, etc