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

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