Wine Quality Classification — Model Comparison

Short description

• Dataset: Wine Quality (data/WineQT.csv) — multiclass classification of the quality label (scores 3–8, imbalanced classes).
• Models: DecisionTree, kNN, RandomForest — 3 hyperparameter variants each (9 classifiers in total).
• Prediction ensemble: VotingClassifier with majority voting (voting="hard") over the same 9 pipelines.
• Validation: stratified 5-fold CV (StratifiedKFold(shuffle=True, random_state=42)).
• Metrics: accuracy (the fraction of correct predictions) and balanced_accuracy (mean per-class recall — important given the imbalanced distribution of wine scores).
• Stack: scikit-learn, Pandas (groupby, to_latex), Plotly (charts), Streamlit (study walkthrough, term definitions, requirements compliance, .tex preview).

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.

Requirements

• Python 3.10+ (recommended)
• Install: pip install -r requirements.txt

Experiment (writes to results/)

Runs the validation and saves, among others, CSV, .tex, wersje_bibliotek.txt and HTML charts in results/wykresy/.

python run_experiment.py

Streamlit and 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

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.

Repository structure

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.

License

See the LICENSE file.