Building Predictive Models of Agricultural Commodity Prices Based on Machine Learning Methods

Abstract

The agricultural sector is volatile, requiring robust predictive models to forecast commodity prices. This study uses machine learning methods to build predictive models using various algorithms. The research uses historical price data and climatic data to identify patterns influencing market trends. Machine learning models significantly outperform traditional statistical methods, providing actionable insights for farmers, traders, and policymakers. Future research should explore real-time data integration and adaptive models for dynamic market conditions.