mdzaheerjk/Drinks-Quality-Prediction-System
This project aims to build a robust, end-to-end Machine Learning pipeline for predicting the quality of Drinks based on physicochemical tests. It demonstrates a complete ML workflow, emphasizing modularity, reproducibility, and automation.
This system helps food scientists, quality control managers, or beverage manufacturers predict the quality of drinks. By inputting physicochemical test results, it provides an assessment of the drink's quality. This enables quicker decisions on product batches.
Use this if you need to automate and standardize the prediction of drink quality based on lab test data, ensuring consistent results.
Not ideal if you're looking for a system to analyze sensory data or consumer feedback, rather than objective chemical measurements.
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MIT
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Last pushed
Jan 27, 2026
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