ANALYSIS OF FOOD PROCESSING DATA USING MACHINE LEARNING: A DECISION TREE APPROACH

Abstract

Abstract— Machine learning (ML) has become an essential tool in the food processing industry, enabling better decision-making and enhancing productivity, quality control, and resource optimization. This paper presents a detailed analysis of food processing data using the Decision Tree (DT) algorithm, a widely used ML technique. The objective of this study is to predict and optimize various aspects of food production such as quality classification, spoilage detection, and process optimization. The study utilizes a publicly available food dataset to illustrate how decision trees can be applied to solve practical challenges in the food industry.