📊 Seaborn — Beginner-to-Pro (EDA Reference) 🎯

A practical notebook for Exploratory Data Analysis (EDA) with Seaborn: beginner basics → customization → advanced patterns.

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✅ What’s inside

• 🧭 Clean, modular examples you can reuse in real projects
• 🧱 A compact EDA mini-pipeline (Titanic)
• 🧩 Version-safe snippets (Seaborn 0.11 vs 0.12+)
• 📁 Lightweight repo layout (data/raw, artifacts) for optional extensions

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🖼️ Example plots

• Distribution with histogram + KDE
• FacetGrid of penguins by island × sex
• Pairplot of Iris dataset
• Hexbin for dense scatter clouds
• Correlation heatmap with upper-triangle mask

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📂 Data

This notebook uses Seaborn built-in sample datasets:

• tips, penguins, flights, iris, titanic

Note: sns.load_dataset() may need internet on first run (dataset download).
If you want to run fully offline, place your own CSV under data/raw/.

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📁 Repo layout

.
├── seaborn-beginner-to-pro-clean-eda-guide.ipynb
├── data/
│   └── raw/               # optional local datasets (kept out of git)
├── artifacts/             # optional exported figures / outputs
├── repo_utils/
│   └── pathing.py         # local + Kaggle path helpers (optional)
├── CASE_STUDY.md
├── requirements.txt
├── README.md
└── LICENSE

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🚀 Run locally

python -m venv .venv
# Windows: .venv\Scripts\activate
# macOS/Linux: source .venv/bin/activate

pip install -r requirements.txt

Open the notebook and run top-to-bottom in:

• Jupyter / JupyterLab
• VS Code notebooks

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🧾 Case Study

See: CASE_STUDY.md (project story + key decisions).