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:Predicts wine quality (0-10 scale) from 11 physicochemical properties (acidity, sulphates, alcohol content, etc.) using the UCI Red Wine Quality dataset (1,599 samples).
Pipeline
Data Ingestion → Validation → Transformation → Model Training → Evaluation Tracked with MLflow.
Results
Initially trained with ElasticNet (R² = 0.28). Switched to a RandomForestRegressor, improving R² to 0.53 and reducing RMSE by ~19%.
The network for creativity
Join 1.25M professional creatives like you
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Creatives on Contra have earned over $150M and we are just getting started
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Predicts wine quality (0-10 scale) from 11 physicochemical properties (acidity, sulphates, alcohol content, etc.) using the UCI Red Wine Quality dataset (1,599 samples).
Pipeline
Data Ingestion → Validation → Transformation → Model Training → Evaluation Tracked with MLflow.
Results
Initially trained with ElasticNet (R² = 0.28). Switched to a RandomForestRegressor, improving R² to 0.53 and reducing RMSE by ~19%.
