https://github.com/inesruizblach/data-science-project

A data science project exploring Portuguese "Vinho Verde" wine quality prediction. Features EDA, feature engineering, ML models, and evaluation using Python, pandas, scikit-learn, and visualization tools.
https://github.com/inesruizblach/data-science-project

binary-classification classification data-science exploratory-data-analysis feature-engineering imbalanced-learn jupyter-notebook machine-learning model-evaluation pandas regression scikit-learn seaborn uci-dataset wine-quality

Last synced: about 1 month ago
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A data science project exploring Portuguese "Vinho Verde" wine quality prediction. Features EDA, feature engineering, ML models, and evaluation using Python, pandas, scikit-learn, and visualization tools.

• Host: GitHub
• URL: https://github.com/inesruizblach/data-science-project
• Owner: inesruizblach
• Created: 2025-10-01T14:58:08.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2025-10-02T11:03:16.000Z (8 months ago)
• Last Synced: 2025-10-09T12:09:59.100Z (8 months ago)
• Topics: binary-classification, classification, data-science, exploratory-data-analysis, feature-engineering, imbalanced-learn, jupyter-notebook, machine-learning, model-evaluation, pandas, regression, scikit-learn, seaborn, uci-dataset, wine-quality
• Language: Jupyter Notebook
• Homepage:
• Size: 4.12 MB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Wine Quality Prediction Project

This repository contains a Jupyter Notebook for predicting the quality of Portuguese "Vinho Verde" wines using machine learning.

> **Note:** This project was completed as part of a university coursework.

## Contents

- Exploratory Data Analysis (EDA)

- Feature engineering

- Model training and evaluation (classification and regression)

- Visualizations

- Conclusions

## Tech Stack

- Python 3.8+

- Jupyter Notebook

- pandas

- numpy

- seaborn

- scikit-learn

- imbalanced-learn

## Key Visualizations

### Wine Quality Distribution

Red and white wine quality scores are imbalanced, with most samples rated 5 or 6.

![Red wine quality distribution](figures/red_quality_distribution.png)

![White wine quality distribution](figures/white_quality_distribution.png)

### Correlation Heatmaps

Chemical properties show strong correlations, e.g., free/total sulfur dioxide and residual sugar/density.

![Red wine correlation heatmap](figures/red_correlation_heatmap.png)

![White wine correlation heatmap](figures/white_correlation_heatmap.png)

### Quality by Alcohol Category

Alcohol content is discretized into low, mid, and high. Most wines with mid alcohol content are rated 5 or 6.

![Red wine quality by alcohol](figures/red_quality_by_alcohol.png)

![White wine quality by alcohol](figures/white_quality_by_alcohol.png)

### Quality by Sweetness

Sweetness (dry/sweet) is associated with quality. Sweet wines tend to have higher quality scores.

![Red wine quality by sweetness](figures/red_quality_by_sweetness.png)

![White wine quality by sweetness](figures/white_quality_by_sweetness.png)

### Model Performance

Confusion matrices for classification models and regression error distributions show model accuracy and error spread.

![Red wine confusion matrix](figures/red_confusion_matrix.png)

![White wine confusion matrix](figures/white_confusion_matrix.png)

![Red wine regression error](figures/red_regression_error.png)

![White wine regression error](figures/white_regression_error.png)

## Conclusions

- Alcohol and sweetness are moderately correlated with wine quality.

- Highly correlated features (e.g., free/total sulfur dioxide, residual sugar/density) can be dropped to improve model interpretability.

- Logistic Regression and SVM models achieve good accuracy for binary classification of wine quality.

- Linear Regression models provide reasonable predictions for continuous quality scores.

- Hyperparameter tuning and cross-validation help prevent overfitting and improve model reliability.

## Usage

1. Clone the repository.

2. Open the notebook in Jupyter.

3. Run all cells to reproduce the analysis and results.

## Dataset

The notebook uses the publicly available "Vinho Verde" wine quality datasets (red and white) from the UCI Machine Learning Repository.

## License

This project is for educational purposes.