Team: · Semester: · Course: Introduction to Data Science (3rd semester)

Summary (3–4 sentences)

Findings (claim → number)

Data

• Source:
• License:
• Retrieval: · retrieved
• Shape: · cached in data/

How to run

python -m venv .venv
# Windows: .venv\Scripts\pip install -r requirements.txt
.venv/bin/pip install -r requirements.txt
.venv/bin/streamlit run app.py        # the app
.venv/bin/jupyter lab eda.ipynb       # the analysis

Everything works offline after the first data fetch.

Repository layout

data_loader.py    # fetch → clean → cache
eda.ipynb         # cleaning + EDA (executed)
models.ipynb      # ML model + evaluation (executed)
app.py            # Streamlit app
data/             # cached raw + clean CSVs
report.md         # full report incl. ethics reflection
proposal.md       # original proposal

Model & evaluation

• Model:
• Metric:
• Baseline:
• Interpretation: <1–2 sentences>

Known issues / limitations

• · ·

AI-use disclosure

Reproducibility

• Python ; pinned requirements.txt.
• SEED = 42 in every notebook; two consecutive Restart & Run All runs produce identical numbers (verified ).