Predictive Analysis of Coconut Prices in Kerala Markets Using Random Forest Regression

This study embarks on a novel endeavor to predict coconut prices in Kerala's markets using Random Forest Regression, addressing the significant fluctuations influenced by weather conditions, demand-supply dynamics, and market trends.Recognizing the critical role of coconuts in Kerala's agriculture and commerce, and the broader implications for stakeholders, this research leverages historical price data to develop a robust predictive model.The model's utility lies in its capacity to aid stakeholders-from farmers to policymakers-in making informed decisions, thereby facilitating more stable market conditions and strategic planning.Through rigorous testing and validation, the study not only assesses the model's accuracy and reliability but also showcases its superiority over existing models, including a wavelet transformation-based model developed in 2018.By employing a more recent and comprehensive dataset, alongside advanced machine learning techniques, this research contributes a significant advancement in the field of agricultural economics and price prediction, offering a practical tool for navigating the complexities of the coconut market in Kerala.