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Milk Quality Prediction Using Machine Learning

B.KUMARI , ADDAGARLA CHANDINI

Assistant professor, 2 MCA Final Semester, Master of Computer Applications,

Sanketika Vidya Parishad Engineering College, Vishakhapatnam,

Andhra Pradesh, India.

Abstract
This project intends to create a robust machine learning-based model for milk quality prediction with a publicly available dataset from Kaggle. The entire process was conducted in the Jupyter Notebook environment, where preprocessing, analysis, and visualization were performed with basic Python libraries such as NumPy, Pandas, Matplotlib, and Seaborn. The objective was to study trends within the data and establish the significant factors influencing milk quality. Multiple classification algorithms from the Scikitlearn library were utilized and compared, including Logistic Regression, Decision Tree, Support Vector Machine, K-Nearest Neighbors, Naive Bayes, Random Forest, and XGBoost. After their performance analysis with different metrics, Random Forest was identified as the most accurate and robust model for the task. To further enhance its predictive capabilities for optimization, hyperparameter tuning was performed with the Optuna library, which is fast and efficient for optimization. The final, optimized model was Pickled for deployment. To make the system easy to use and user-friendly, a web application with a clean design was created with HTML and CSS for the frontend, and Flask was utilized as the backend framework for serverside logic management and model integration. Users can input certain parameters of milk through the web interface and get instant predictions for the quality of the milk. This project demonstrates a complete machine learning pipeline, from data exploration and model selection to optimization and deployment, reflecting the practical application of AI in food quality inspection and decision support systems.
Index Terms- Milk Quality Classification,Supervised Machine Learning,Random Forest Optimization, Scikit-learn Classifiers, Hyperparameter Tuning with Optuna, Label Encoding for Classification, Ensemble Learning Models, Food Safety and Dairy Technology, Model Evaluation Metrics (Accuracy, Cross-Validation), Pickle Model Serialization for Deployment