Agricultural Price Forecasting Using Flask Python Application
Keywords:
Auto-Regressive Integrated Moving Average (ARIMA), Forecasting, Generalized Neural Network (GRNN), Machine learning, Models, Partial Dependency Plots (PDPs)Abstract
Flask is a Python web development framework that implements a predictive model for commodity futures. The system gathers data from various commodity futures databases, applies machine learning algorithms to analyze it, and provides users with projections of future price movements. Users can access commodity profiles, current pricing, and predictions for various agricultural products through a web interface. The system utilizes decision tree regression models to forecast future prices by considering underlying factors influencing agricultural markets and historical data. This integration of machine learning algorithms and online technologies enhances the stability and efficiency of market economies. It enables stakeholders to make well-informed decisions in the trade of agricultural commodities, thereby improving overall market operations. By providing accurate and timely information, the system supports better strategic planning and risk management for farmers, traders, and investors. The seamless combination of predictive analytics and user-friendly web interfaces represents a significant advancement in agricultural market forecasting.
