Milk Quality Detection Using Machine Learning

Poor-quality milk and the use of chemicals in it can lead to serious health problems, including various diseases and, in some cases, even death for those who consume it. In our society, using such products or contaminated milk that contains chemicals or is of bad quality, often with water or other adulterants, is very common. Based on previous research and existing models, we have improved the process to better and more accurately predict milk quality by using a voting system. This system uses four different algorithms: KNN (K Nearest Neighbour), Naïve Bayes, Random Forest, and Decision Tree. We applied these models to a dataset with almost 1000 samples. To enhance performance, we used brute-force feature selection and a voting process to make accurate decisions. All these procedures were implemented in RapidMiner Studio, resulting in an overall accuracy of 99.69%.