Developement of AI-ML Based Models for Predicting agri-horticultural Commodities

The project focuses on developing an AI–ML-based framework for predicting the prices of essential agri-horticultural commodities in India. It uses Random Forest and XGBoost models trained on data from 550 government reporting centers to forecast prices accurately. The system integrates data preprocessing, model training, and a Streamlit-based web interface for real-time, user-friendly predictions that support data-driven market analysis and policy decisions.