Predictive Analytics for Milk Quality Using Random Forest (RF) Algorithm

This paper introduces a machine learning-based framework for assessing milk quality, utilizing a Random Forest (RF) algorithm to analyze six key parameters: pH value, temperature, color, taste, odor, and turbidity . These parameters are used to categorize milk into three quality grades—low, medium, and high—within a user-friendly web application that facilitates real-time predictions. The proposed system demonstrates high accuracy and offers a rapid, cost-effective alternative to traditional laboratory methods, making it a valuable tool for dairy producers and quality control professionals . By democratizing access to sophisticated quality assessments, the system ensures that only high-quality milk reaches consumers. Future work will explore dataset expansion, advanced machine learning techniques, and IoT integration for continuous real-time monitoring, highlighting the potential of machine learning to transform dairy industry practices.