Prediction of Beef Production Using Linear Regression, Random Forest and k-Nearest Neighbors Algorithms

Kahramanmaraş Sütçü İmam Üniversitesi Tarım ve Doğa Dergisi, Journal Year: 2025, Volume and Issue: 28(1), P. 247 - 255

Published: Jan. 30, 2025

The rapid increase in the global population and evolving dietary habits have significantly heightened demand for high-quality protein sources. Beef, as a vital source, plays crucial role meeting this growing demand. This study aims to develop evaluate machine-learning model predict beef production using meteorological, agricultural, economic data. To achieve this, three different machine learning algorithms—Linear Regression, Random Forest, k-Nearest Neighbors—were employed. results indicate that Forest algorithm outperformed other methods terms of R² error metrics, demonstrating superior predictive accuracy. highlights potential techniques predicting production, offering valuable insights stakeholders involved strategic decision-making meet nutritional needs. As continues rise, importance such models becomes increasingly significant, emphasizing distinct advantages approaches provide context.

Language: Английский

Mastitis diagnosis with machine learning algorithms

Neural Computing and Applications, Journal Year: 2025, Volume and Issue: unknown

Published: March 29, 2025

Language: Английский