Problem & context
Problem
Build a reproducible regression workflow that predicts the quality score of red wine from measured chemical properties, while organising the work as an installable Python package.
Intended users
Developers and reviewers interested in a structured end-to-end machine-learning regression workflow.
Role and contributions
Package structure, development environment and initial data exploration
Dataset / inputs
The repository includes a red-wine CSV with 11 physicochemical predictors, including acidity, sugar, chlorides, sulphates, alcohol and density, plus a quality target column.
Methods & architecture
The current repository establishes a Python 3.9 Conda environment, an editable mlproject package and a modular layout for configuration, components, entities, utilities, pipelines, research and a future Flask interface. The committed demo currently loads and previews the dataset with pandas.
Implementation
- Add the red-wine quality dataset
- Create the reusable src/mlproject package structure
- Define configuration, component, entity, utility and pipeline modules
- Add environment and dependency setup instructions
- Verify dataset loading with pandas
Current evidence
The current implementation verifies the project foundation below; broader outcome claims will be added after evaluation.
Limitations & next steps
The public repository is currently a project scaffold. Its model, training pipeline, configuration files, tests, evaluation metrics and Flask application have not yet been implemented in the committed source, so no predictive-performance claim is made.
Implement validation and preprocessing, compare regression models with cross-validation, add automated tests, record evaluation metrics and connect the selected model to the application interface.
Attribution & licence
No licence file is currently present in the repository.