🌍 Building-EPC

Tools and resources for analyzing Building Energy Performance Certificates (EPCs) — with a focus on the Barcelona dataset. The repo contains Python scripts, Jupyter notebooks, data files, and figures to let you explore, model, and visualise EPC data end-to-end.

---

📑 Table of Contents

• Project Structure
• Prerequisites
• Getting Started
  • First-time Git quick-start
  • Cloning the repository
  • Opening in VS Code
  • Installing dependencies
• Usage
• Typical Git Workflow
• Contributing
• License

---

🗂️ Project Structure

``` Barcelona_ANN_new.py # Main Python script for ANN modelling Barcelona_ANN.ipynb # Jupyter notebook for ANN analysis Masters_Code2.ipynb # Additional notebook experiments CE3X_details.md # Notes on the CE3X EPC tool energy_efficiency_certificates.csv # Main dataset dataset_schema.csv # Column descriptions figs/ # Pre-generated plots and charts README.md # You are here ```

---

🛠️ Prerequisites

• Python 3.9+
• Git (any recent version)
• VS Code (optional but recommended) with
  • Python extension
  • Jupyter extension
  • GitHub Copilot or ChatGPT Code Editor for AI coding assistance

Why VS Code?
It bundles Git integration, a first-class Jupyter experience, and lets you use Copilot/LLM extensions that autocomplete or explain code as you type.

---

🚀 Getting Started

First-time Git quick-start

1. Install Git: download from https://git-scm.com and follow the defaults.
2. Set your name & email (one-off):
   ```bash git config --global user.name "Your Name" git config --global user.email "you@example.com" ```
3. Generate an SSH key (optional but nicer than HTTPS passwords):
   ```bash ssh-keygen -t ed25519 -C "you@example.com" ``` Add the public key (`~/.ssh/id_ed25519.pub`) to GitHub → Settings → SSH keys.

After that, every repo follows the same three-step loop: clone → edit → commit & push.

---

1. Clone the repository

git clone https://github.com/Dumo1999/Building-EPC.git
cd Building-EPC

2. Open the project in VS Code (recommended)

code .

• VS Code will detect the Python env and suggest installing extensions.
• If you’ve enabled GitHub Copilot or another LLM helper, you’ll get smart autocompletion and inline explanations as you work.

3. Install required packages

If a `requirements.txt` is present:

pip install -r requirements.txt

Otherwise, install the common stack manually:

pip install numpy pandas scikit-learn matplotlib seaborn tensorflow shap

(Tip: use a virtual environment — `python -m venv .venv` then `source .venv/bin/activate`)

---

🎛️ Usage

Task	Command / Action
Run main script	`python Barcelona_ANN_new.py`
Open notebooks	`jupyter lab` or VS Code Jupyter view
View figures	Browse `figs/` or load them in any image viewer

---

🔄 Typical Git Workflow

Repeat this loop each time you make edits.

# See what changed
git status

# Stage only the files you want
git add path/to/file.py AnotherFile.ipynb

# Commit with a clear message
git commit -m "Fix: cleaner plotting function for ANN results"

# Push to GitHub
git push origin main   # or your feature branch

Need a refresher? GitHub has a great beginner guide:
https://docs.github.com/en/get-started/quickstart

---

🤝 Contributing

Pull requests are welcome! For larger ideas, open an issue first so we can discuss direction and avoid duplicate work.

---

📜 License

Distributed under the MIT License. See `LICENSE` for details.

---