⚡ EV Charging EDA (Global 2025)

Decision-ready exploratory analysis for global EV charging infrastructure + EV models datasets.

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📌 What’s inside

• 📓 Notebooks:
  • ev-charging-stations-eda.ipynb (stations + country summaries + light ML table)
  • global-ev-charging-stations-models-eda-tutorial.ipynb (2025 refresh + models focus)
• 🧱 Lightweight repo layout: data/raw + artifacts
• 🧭 Path-safe loading (local data/raw + Kaggle fallback)

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📁 Repo layout

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├── ev-charging-stations-eda.ipynb
├── global-ev-charging-stations-models-eda-tutorial.ipynb
├── data/
│   └── raw/               # put CSVs here locally
├── artifacts/             # saved outputs (optional)
├── repo_utils/
│   └── pathing.py         # local + Kaggle path helper
├── CASE_STUDY.md
├── requirements.txt
└── .gitignore

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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 either notebook in Jupyter / VS Code and run top-to-bottom.

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📦 Data (local + Kaggle)

Local (recommended):

• Put the CSV files in data/raw/
• The notebook loads them via:
  • resolve_data_path("<file>.csv", kaggle_subdir_hint="global-ev-charging-stations")

Kaggle:

• Works with /kaggle/input/global-ev-charging-stations/...

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🧾 Case Study

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

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📜 License

MIT (see LICENSE)