A Data-Driven Approach for Milk Quality Prediction using Machine Learning Techniques
Download| Volume 6 Issue 1, 2025 | |
|---|---|
| Author(s): |
Muhammad Ayoub Kamal DHA Suffa University, Karachi & Multimedia University, Cyberjaya, Malaysia , ayoub.kamal@mmu.edu.my Huma Jamshed DHA Suffa University, Karachi , huma.jamshed@dsu.edu.pk Urooj Waheed DHA Suffa University, Karachi , urooj.waheed@dsu.edu.pk Yusra Mansoor DHA Suffa University, Karachi , yusra.mansoor@dsu.edu.pk Laiq Muhammad Khan Institute of Business Management (IoBM), Karachi, laiq.muhammad@iobm.edu.pk |
| Abstract | Machine learning-based approaches can be extremely helpful in monitoring the quality of products and making rapid decisions in the food sector, where maintaining standards is crucial. Even a single gram of milk with poor quality can degrade large quantities, leading to significant financial losses. Contaminated milk can harbor millions of bacteria within just a few hours, posing serious health risks to consumers. Therefore, to ensure milk quality, it must be thoroughly examined for the presence of essential components and any potential contaminants. In this study, machine learning algorithms were employed to assess milk quality. Seven factors were considered for evaluation, and the dataset was sourced from the publicly accessible Kaggle data portal. The milk samples were classified into low, medium, and high-quality categories based on these seven characteristics. The K-Nearest Neighbor, Naive Bayes, Multilayer Perceptron, and Support Vector Machine techniques were utilized for classification and estimation. The findings of each method were presented and compared, demonstrating the classification accuracy achieved. |
| Keywords | Milk Quality, Machine Learning, Quality Prediction, KNN Prediction, Data Driven, Naive Bayes, Multilayer Perceptron, Support Vector Machine |
| Year | 2025 |
| Volume | 6 |
| Issue | 1 |
| Type | Research paper, manuscript, article |
| Recognized by | Higher Education Commission of Pakistan, HEC | Category | Journal Name | ILMA Journal of Technology & Software Management | Publisher Name | ILMA University | Jel Classification | -- | DOI | - | ISSN no (E, Electronic) | 2790-590X | ISSN no (P, Print) | 2709-2240 | Country | Pakistan | City | Karachi | Institution Type | University | Journal Type | Open Access | Manuscript Processing | Blind Peer Reviewed | Format | Paper Link | https://ijtsm.ilmauniversity.edu.pk/arc/Vol6/i1/pdf5.pdf | Page | 37-44 |