Optimizing Chili Price Prediction Using Machine Learning Classification

Abstract

Optimizing chili price prediction is critical for agricultural stakeholders, enabling better decision-making in supply chain management, market strategies, and farming practices. This research focuses on leveraging machine learning classification models to improve the accuracy and reliability of chili price predictions. The research addresses the challenges of class imbalance, which often occurs due to the uneven representation of price fluctuations in datasets. Resampling techniques, including oversampling the minority class with Synthetic Minority Oversampling Technique (SMOTE) and undersampling the majority class, were employed to balance the dataset and enhance the model's sensitivity to less frequent price drops. Key predictive features such as weather conditions, market demand, transportation costs, and economic indicators were integrated into the models. Advanced classification algorithms like Random Forests and Gradient Boosted Trees were utilized, demonstrating their effectiveness in handling non-linear relationships and class imbalance. Regularization techniques and k-fold cross-validation were applied to prevent overfitting and ensure robust model performance across different data subsets.The results show significant improvements in precision, recall, and overall model accuracy, making the approach suitable for real-world applications. By optimizing machine learning models, this research provides actionable insights for stakeholders to manage price volatility effectively, supporting sustainable agricultural practices and market stability.