A comparative analysis of the machine learning methods for milk adulteration detection.

Milk adulteration becomes a great business in the dairy industry where its quality is compromised by adding certain chemicals or other substitutions. Milk is one of the most important nutritious food products that is consumed by humans irrespective of age from babies to older people. The consumption of adulterated milk can impair the functioning of various organs of the body, causing heart problems, cancer, and in extreme cases, even death. World Health Organization highlighted that if adulteration is not put to a stop, a lump of India's population would be suffering from serious and fatal diseases like cancer by the end of 2025. Hence there is a huge demand for a cutting-edge research to detect the adulterated milk. A comparative analysis of five machine learning algorithms i.e., Logistic Regression, Naive Bayes, Random Forest, Support Vector machine and Gradient Boosting Machine are presented in this paper to classify the pure and adulterated milk. [ABSTRACT FROM AUTHOR]