Application of Ensemble Method to Predict Individual Pork Prices Using Multi-SourceInformation

The Pork Price Prediction System is a web-based application developed using Flask and SQLite, designed to forecast pork market prices based on multiple production and economic factors. The system integrates a Random Forest Regressor machine learning model to predict prices with high accuracy, leveraging key input parameters including animal weight, backfat thickness, muscle percentage, feed price, market search volume, and electricity cost. Users can register, log in, and access a user-friendly interface where they input production data to receive instant price predictions. The system also supports batch processing through CSV file uploads, enabling users to analyze multiple records simultaneously. All predictions are stored in a SQLite database, allowing users to track their prediction history and view statistical summaries on a personalized dashboard. The model's performance is evaluated using metrics such as R² score (0.9929), RMSE (41.95), and feature importance analysis, providing users with transparency into which factors most influence price outcomes. Additional features include secure user authentication with password hashing, session management, and a dedicated model information page displaying performance metrics and feature contributions. This system can be utilized by pork farmers, livestock traders, agricultural cooperatives, and market analysts to make data-driven decisions regarding production planning, inventory management, and market timing. With its scalable architecture and integration capabilities, the Pork Price Prediction System offers an effective, data-driven solution for navigating the complexities of livestock market dynamics.

Keywords:

Pork Price Prediction, Machine Learning, Random Forest Regressor, Flask Web Application, Agricultural Technology, Livestock Market Analysis, Price Forecasting, Ensemble Learning, SQLite Database, Predictive Analytics, Agricultural Decision Support, Data-Driven Farming, Swine Production, Market Price Prediction, Feature Importance Analysis, Batch Processing, CSV Data Upload, User Authentication, Web-Based Prediction System, Livestock Economics, Agricultural Informatics, Smart Farming, Pork Industry, Price Modeling, Production Optimization