Commodity Price Prediction Using Machine Learning: Insights, Challenges, and Prospect

Price fluctuations in horticultural commodities such as pulses, potatoes, and onions present a critical challenge to the agricultural sector, impacting market stability and food security. Traditional forecasting techniques often fail to account for the complexities introduced by dynamic variables, including weather patterns, supply-demand shifts, and international trade policies. Recognizing the potential of Machine Learning in addressing these limitations, this study explores an advanced ML-based framework for accurate commodity price prediction. Through rigorous experimentation and comparative analysis, the proposed model integrates time-series data, supervised learning techniques, and multidimensional datasets to enhance predictive accuracy. The effectiveness of this approach is assessed using historical price data, enabling insights into market trends and volatility. By equipping stakeholders—from farmers to policymakers—with actionable intelligence, this research contributes to the development of more resilient agricultural markets and supports informed decision-making in the pursuit of global food security.