Supervised Machine Learning Modeling (Classification and Regression) for Wine Quality Prediction.
Explore a dataset of 🍷 Red Wines 🍷 from Potuguese's "Vinho Verde" type. The dataset indicates the quality score (0-10) given via sensorial to each wine.
Create two Machine Learning Models. One to predict the quality score and the other to classify a wine to "good" or "bad". The Models to be used are from Linear and Logistic Regression.
- Regression:
- R2: 0.4153
- RMSE: 0.6182
- MAE: 0.4970
- MSE: 0.3821
- Classification:
- Accuracy: 0.7469
- Precision: 0.7917
- Recall: 0.7430
- F1: 0.7666
- AUCROC: 0.8202
| Column | Description |
|---|---|
| fixed_acidity | Fixed acidity level in the wine |
| volatile_acidity | Volatile acidity level in the wine |
| citric_acid | Citric acid content in the wine |
| residual_sugar | Residual sugar content in the wine |
| chlorides | Chloride concentration in the wine |
| free_sulfur_dioxide | Free sulfur dioxide concentration in the wine |
| total_sulfur_dioxide | Total sulfur dioxide concentration in the wine |
| density | Density of the wine |
| pH | pH level of the wine |
| sulphates | Sulfate concentration in the wine |
├── LICENSE
├── README.md <- The top-level README for developers using this project.
├── data
│ └── raw <- The original, immutable data dump.
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ and a short `-` delimited description, e.g.
│ `1.0--creating-the-model.ipynb`.
├── references <- images, reports, and other resources for the project