MILK QUALITY PREDICTION USING RAPID MINER AND ML ALGORITHMS

Milk is an essential source of nutrition for humans, and its quality is crucial for ensuring food safety and public health. The quality of milk is dependent on various factors, such as animal health, feeding practices, and environmental conditions. Developing a predictive model to assess the quality of milk using machine learning techniques. This study aims to investigate the potential of using RapidMiner and R language to predict the quality of milk using a random forest algorithm. The dataset used in this study includes various parameters related to milk quality, such as fat content, protein content, lactose, and somatic cell count. The data is preprocessed and then fed into the random forest algorithm for training and testing the model. The results of the study demonstrate that the developed model is capable of accurately predicting milk quality, which can be beneficial for the dairy industry to ensure milk quality and enhance the product’s overall value.