Paper Contents
Abstract
The fluctuation in crop prices significantly impacts agricultural stakeholders, including farmers, traders, and policymakers. This study presents a machine learning-based approach to predict crop prices using historical data. By leveraging advanced regression models and time-series analysis, the system forecasts price trends with a focus on accuracy and scalability. The research highlights the integration of publicly available datasets, preprocessing techniques, and feature engineering to derive meaningful insights. The proposed model facilitates informed decision-making, ultimately enhancing market stability and supporting the agricultural economy. The findings underscore the potential of predictive analytics in transforming traditional agricultural practices.
Copyright
Copyright © 2024 Uday P . This is an open access article distributed under the Creative Commons Attribution License.
