This research introduces a novel approach for enhanced price prediction of seasonal agricultural products through the innovative integration of ensemble learning methodologies. By harnessing the combined strengths of Naive Bayes (NB), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost) algorithms within an ensemble framework, this model endeavors to revolutionize the accuracy and reliability of price forecasting in the agricultural domain. Leveraging a comprehensive dataset comprising historical price data and a diverse array of features including weather patterns, seasonal dynamics, and economic indicators specific to agriculture, our ensemble technique aims to transcend conventional forecasting limitations. Through meticulous preprocessing, strategic feature engineering, and model training, our methodology aspires to deliver superior predictive capabilities, offering invaluable insights essential for optimizing planting cycles, harvesting strategies, and informed decision-making for stakeholders, thereby advancing market predictions and resource allocation in the realm of seasonal agricultural commodities.

Seasonal Agricultural Products, Support Vector Machine, Naive Bayes, Machine Learning, eXtreme Gradient Boosting, Predictive Analytics.