Enhancing Milk Quality Prediction with Feature Engineering and Supervised Learning

This paper presents a study on enhancing milk quality prediction with feature engineering and supervised learning. Milk quality is an essential factor for dairy industry stakeholders, as it directly affects the profitability and customer satisfaction. However, predicting milk quality accurately is a challenging task due to the complex nature of milk composition and various influencing factors, such as environmental and animal-related factors. To address this issue, we propose a supervised learning-based approach that leverages feature engineering techniques to extract informative features from milk composition data and other relevant parameters. We evaluate the proposed approach on a real-world dataset of milk samples collected from a dairy farm and compare it with several state-of-the-art supervised learning models. The experimental results show that the proposed approach outperforms other models in terms of predictive accuracy, indicating the effectiveness of feature engineering in improving milk quality prediction. Moreover, we conduct a feature importance analysis to gain insights into the factors that contribute most to milk quality and provide recommendations for dairy farmers to improve milk quality. The proposed approach can be used as a decision support system for dairy industry stakeholders to make informed decisions and optimize milk quality control processes.

Keywords: Machine Learning, Adaboost , Extra tree, Hybrid Model ,ML techniques, evaluation.