Machine Learning-Based Classification for Milk Quality Assessment

Milk is an essential nutrient source, but its quality assurance is a significant challenge since issues like adulteration, bacterial contamination, and spoilage largely pose health risks and considerable economic losses. This project aims at developing a machine learning-based milk quality prediction system to classify milk into three categories Low, Medium, and High based on measurable characteristics like pH, temperature, odor, and color. This proposed system makes use of the latest AI/ML models such as Decision Tree, K-Nearest Neighbors (KNN), Naive Bayes, and Support Vector Machine (SVM) in order to achieve greater accuracy and reliability of the prediction. Our system has obtained a highest accuracy and it showed promise for offering a strong and effective tool for the dairy industry. This resource ensures the consumers' safety and integrity of the products by allowing detection of poor quality milk early, which prevents health hazards and minimizes economic loss.