galafis/python-ml-pipeline-complete
Data Science project - python-ml-pipeline-complete
What's novel
Data Science project - python-ml-pipeline-complete
Code Analysis
15 files read · 4 roundsA basic scikit-learn classification pipeline that loads a CSV, applies StandardScaler/OneHotEncoder via ColumnTransformer, trains one of three sklearn classifiers, evaluates with standard metrics, and serves predictions via a minimal FastAPI endpoint.
Strengths
Clean separation of concerns with well-documented modules following scikit-learn's BaseEstimator/TransformerMixin pattern, and reasonable unit/integration test coverage for the components that exist.
Weaknesses
The README and config files describe a production MLOps framework with Evidently, MLflow, Optuna, XGBoost, LightGBM, and PostgreSQL, but none of these are implemented or even in requirements.txt — the actual code is a ~500-line tutorial-level sklearn wrapper with 6-line and 10-line stubs for monitoring and dashboard.
Score Breakdown
Signal breakdown
Innovation
Craft
Traction
Scope
Evidence
Commits
64
Contributors
1
Files
38
Active weeks
8
Repository
Language
Python
Stars
1
Forks
0
License
MIT