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.

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Experimental

The pipeline implements a modular architecture with separate stages for data ingestion, validation, transformation, and model training, utilizing scikit-learn for feature engineering and multiple classifier algorithms with hyperparameter tuning. It includes automated data quality checks, experiment tracking, and model versioning to ensure reproducibility across pipeline runs. Integration with MLflow enables centralized tracking of metrics and artifacts, while containerization support facilitates deployment consistency.

No Package No Dependents
Maintenance 10 / 25
Adoption 4 / 25
Maturity 9 / 25
Community 0 / 25

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License

MIT

Last pushed

Jan 27, 2026

Commits (30d)

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