Predicting Milk Quality
Technique Overview
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Language: Python
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This project leverages key packages such as scikit-learn, pandas, and matplotlib for data preprocessing, classification modeling, and visualization.
Dataset
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The dataset, sourced from Kaggle (Milk Quality Dataset), forms the backbone of this project. It encompasses diverse features like pH, temperature, and color, essential for the supervised machine learning models in predicting milk grades.
Motivation
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Driven by the escalating demand for food quality, this project delves into the realm of milk classification. Ensuring the purity and quality of this essential commodity is crucial for both consumer health and industry credibility.
Project Summary
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Employing supervised machine learning models like K-Nearest Neighbors and Random Forest, the project focuses on predicting milk grades based on attributes like pH, temperature, and color. Through thorough exploratory data analysis, normalization, and addressing class imbalances, the models demonstrate robust performance. The incorporation of SMOTE for class imbalance mitigation further refines the models. The detailed classification report and confusion matrices unveil the models' precision, recall, and accuracy.
Conclusion
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The project's findings underscore the Random Forest's robustness in handling the intricate nature of milk data, with pH emerging as a critical factor in predicting milk quality. The models exhibit commendable recall rates, essential for ensuring precise predictions and minimizing health and business risks. Overall, this project provides valuable insights for optimizing milk quality control processes in the dairy industry.