Wine Quality Classification

🚀 Multiclass wine quality classification with leakage-free scikit-learn pipelines - compare 9 classifier variants and a majority-voting ensemble under stratified cross-validation

An end-to-end machine learning study on the Wine Quality dataset. Nine classifier variants — Decision Tree, k-NN and Random Forest, three hyperparameter settings each — are evaluated with stratified 5-fold cross-validation, alongside a VotingClassifier ensemble that aggregates all nine by majority vote.

The project is built around methodological rigour rather than leaderboard chasing: every scaler lives inside a Pipeline so it can never see a test fold, missing data aborts the run, a single seed drives every random component, and the exact library versions are written to disk with the results. A Streamlit walkthrough presents the study, the glossary, the LaTeX tables and the interactive charts.

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🎯 Key Features

• 🍷 Multiclass target — classifies the quality label (scores 3–8) on data/WineQT.csv, an intentionally imbalanced real-world distribution.
• 🌳 Nine classifier variants — DecisionTreeClassifier, KNeighborsClassifier and RandomForestClassifier, three hyperparameter settings each, for a readable family-vs-family comparison.
• 🗳️ Majority-voting ensemble — a VotingClassifier with voting="hard" over the same nine pipelines, evaluated under identical folds.
• 🔒 Leakage-free by construction — MinMaxScaler sits inside a ColumnTransformer within each Pipeline, so it is fitted only on the training fold.
• 📏 Two metrics, deliberately — accuracy plus balanced_accuracy (mean per-class recall), because accuracy alone flatters a model on imbalanced wine scores.
• 🎲 Reproducible runs — one seed (RANDOM_STATE = 42) shared by every estimator and by StratifiedKFold, with library versions recorded per run.
• 📄 Publication-ready export — results are written as CSV and LaTeX (pandas.DataFrame.to_latex) for direct inclusion in a report.
• 📊 Interactive Streamlit app — four tabs covering the study description, requirements mapping, EDA and results, including a .tex preview.

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📊 Results & Visualizations

Streamlit dashboard

Captured from the container built by .tools/docker/docker-compose.yml.

All charts below are produced by python run_experiment.py and written to results/. Interactive Plotly versions of the same figures are saved as standalone HTML under results/wykresy/.

Class distribution	Correlation matrix

Model comparison	Family aggregates

Numeric results live in results/wyniki_szczegolowe.csv (per-model means and standard deviations) and results/wyniki_agregaty_rodzin.csv (per-family aggregates), with matching .tex tables beside them.

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🏗️ Pipeline

Protection against data leakage

All scaling operations are performed inside a scikit-learn Pipeline object:

Pipeline([
("preprocess", ColumnTransformer([("num", MinMaxScaler(), feature_cols)])),
("clf", classifier),
])

Thanks to this, MinMaxScaler is fitted exclusively on the training fold — it never "sees" the test data before evaluation. Passing a ready Pipeline to cross_validate guarantees this automatically for each of the 5 splits. Details: scikit-learn — Common pitfalls.

No missing data

The wczytaj_pelna_ramke function in src/experiment.py calls df.isna().any().any() and raises an exception if it finds missing values — the experiment will not run on an incomplete dataset.

Reproducibility

In line with the requirement scikit-learn — Getting reproducible results:

• One fixed seed: RANDOM_STATE = 42 in src/config.py; passed to every classifier (random_state) and to StratifiedKFold(shuffle=True, random_state=RANDOM_STATE).
• StratifiedKFold with shuffle=True requires a seed — without it the sample order after shuffling would differ on every run.
• The same cv object is passed to each cross_validate call, which guarantees identical splits for all models.
• The results/wersje_bibliotek.txt file records the versions of scikit-learn, numpy and pandas when the results are generated — making it possible to reproduce the environment.

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🧩 Models Under Comparison

Family	Variants	Hyperparameters
DecisionTree	dt_gleb_5, dt_gleb_15, dt_gleb_25	max_depth 5 / 15 / 25 with min_samples_leaf 2 / 5 / 10
kNN	knn_k5, knn_k11, knn_k21	n_neighbors 5 / 11 / 21, weights uniform / distance / uniform
RandomForest	rf_50, rf_100, rf_200	n_estimators 50 / 100 / 200 with max_depth 10 / 15 / 20
Ensemble	majority_voting_9	VotingClassifier(voting="hard") over all nine pipelines above

Validation is identical for every row: StratifiedKFold(n_splits=5, shuffle=True, random_state=42), scored on accuracy and balanced_accuracy via cross_validate.

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🛠️ Technology Stack

Machine Learning

• scikit-learn (>=1.3,<2) — Pipeline, ColumnTransformer, MinMaxScaler, the three classifier families, VotingClassifier, StratifiedKFold, cross_validate
• NumPy (>=1.24,<3) — numeric aggregation of fold scores

Data & Reporting

• pandas (>=2.0,<3) — data loading, groupby aggregation, CSV export and to_latex table generation

Visualization & UI

• Plotly (>=5.18,<6) — all four figures, exported as interactive standalone HTML
• Streamlit (>=1.28,<2) — the four-tab study walkthrough

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🚀 Getting Started

Prerequisites

• Docker — for the containerised path below (recommended)

• Python 3.10+ (recommended)

• pip and the ability to create a virtual environment

Run with Docker (recommended)

The container installs the scientific stack, runs the experiment and serves the dashboard in one step — no local Python setup and no virtual environment:

docker compose -f .tools/docker/docker-compose.yml up --build

The dashboard is then available at http://localhost:8501.

Generated artefacts (results/, data/) are bind-mounted back to the host, so charts and metrics written inside the container survive it being removed. Stop the stack with:

docker compose -f .tools/docker/docker-compose.yml down

1. Clone the Repository

git clone https://github.com/dawidolko/WineQuality-Classifier-Python.git
cd WineQuality-Classifier-Python

2. Install Dependencies

pip install -r requirements.txt

3. Run

Run the experiment on its own — it writes CSV, .tex, wersje_bibliotek.txt and the HTML charts in results/wykresy/:

python run_experiment.py

Launch the Streamlit application:

streamlit run streamlit_app.py

One-command start scripts

The start.sh (Linux/macOS) and start.bat (Windows) scripts run, in order:

1. venv + pip install -r requirements.txt
2. python run_experiment.py
3. streamlit run streamlit_app.py → usually http://localhost:8501

chmod +x start.sh
./start.sh

start.bat

Application tabs

Tab	Content
Project description	Study goal, experiment flow (what → why → effect), glossary of terms, requirements compliance, LaTeX file preview
Requirements & methodology	Table: requirement → implementation in code / files
Dataset (EDA)	quality class distribution, correlation matrix, data preview
Classification results	CV metric charts, tables, preview and download of results

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📁 Project Structure

WineQuality-Classifier-Python/
├── 📊 data/
│ └── WineQT.csv # Source dataset (Kaggle / yasserh)
├── 🐍 src/
│ ├── config.py # Paths, seed (RANDOM_STATE = 42)
│ ├── experiment.py # Pipeline, CV, ensemble, CSV/LaTeX export
│ └── wykresy.py # Plotly charts + HTML export
├── 📈 results/ # Generated artifacts
│ ├── img_rozkald.png # Class distribution
│ ├── img_korelacja.png # Correlation matrix
│ ├── img_modele.png # Model comparison
│ ├── img_agregaty.png # Family aggregates
│ ├── wyniki_szczegolowe.csv/.tex # Per-model results
│ ├── wyniki_agregaty_rodzin.csv/.tex # Per-family aggregates
│ ├── wersje_bibliotek.txt # Library versions for reproducibility
│ └── wykresy/ # Interactive Plotly HTML charts
├── 📚 docs/
│ ├── diagrams/pipeline.svg # Pipeline diagram
│ ├── build_pptx.py # Presentation generator
│ └── Wine_Quality_Prezentacja.pptx # Generated presentation
├── ▶️ run_experiment.py # Command-line entry point
├── 🖥️ streamlit_app.py # Web application
├── 🚀 start.sh / start.bat # Experiment + Streamlit
├── 📦 requirements.txt
└── 📖 README.md

Repository structure reference

Path	Description
data/WineQT.csv	Source data
src/config.py	Paths, seed
src/experiment.py	Pipeline, CV, ensemble, CSV/LaTeX export, chart invocation
src/wykresy.py	Plotly charts + HTML export
run_experiment.py	Command-line entry point
streamlit_app.py	Web application
start.sh / start.bat	Experiment + Streamlit
results/	Generated results (CSV, TeX, wykresy/*.html, wersje_bibliotek.txt)

.gitignore

Ignored items include .venv/, Python cache, and IDE files. Generated files in results/ can optionally be added to the ignore list — .gitignore contains a ready, commented-out block with instructions.

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📄 License

This project is open source and available under the terms described in the LICENSE file.

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👨‍💻 Author

Created by Dawid Olko

• Website — dawidolko.pl
• LinkedIn — @dawidolko