https://github.com/quantumcoderrr/buildcast
https://github.com/quantumcoderrr/buildcast
Last synced: 5 months ago
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- Host: GitHub
- URL: https://github.com/quantumcoderrr/buildcast
- Owner: QuantumCoderrr
- License: mit
- Created: 2025-08-29T18:59:50.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-08-29T19:45:21.000Z (6 months ago)
- Last Synced: 2025-08-29T21:49:51.618Z (6 months ago)
- Language: Python
- Size: 3.82 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 🏗️ BuildCast - Building Energy Prediction
This project is a **machine learning pipeline** for predicting building energy usage using **synthetic datasets**.
It includes **data preprocessing, model training (LightGBM + PyTorch), evaluation, and visualization** of results.
---
## 📌 Features
- Preprocessing with **StandardScaler** and **OneHotEncoder**
- **LightGBM baseline model** for regression
- **PyTorch neural network model** implementation
- **Evaluation metrics**: MAE, RMSE, R²
- **Graphical visualization** of predicted vs actual values
- **Synthetic dataset generation** with 20,000 data points for better visualization
---
## 📂 Project Structure
📦 Building-Energy-Prediction
- 📜 main.py # Main training + evaluation pipeline
- 📜 requirements.txt # Dependencies
- 📜 buildings.csv # Example dataset
- 📜 README.md # Project documentation
---
## ⚙️ Installation
1. Clone the repository:
```bash
git clone https://github.com/QuantumCoderrr/BuildCast.git
cd BuildCast
```
2. Create a virtual environment & install dependencies:
```bash
python -m venv venv
source venv/bin/activate # On Mac/Linux
venv\Scripts\activate # On Windows
pip install -r requirements.txt
```
---
## ▶️ Usage
1. Run with existing dataset
```bash
python main.py
```
2. Generate & use synthetic dataset (20,000 points)
```bash
python generate_dataset.py
python main.py
```
---
## 📊 Visualization
The model outputs a scatter plot comparing actual vs predicted energy usage.
This helps visualize how well the model generalizes on test data.
## 📈 Example Output
Metrics (MAE, RMSE, R²) printed on console
Scatter plot of predictions vs actual values
## 🛠️ Tech Stack
Python 3.11+
Pandas, NumPy
Scikit-learn
LightGBM
PyTorch
Matplotlib
---
## 👩💻 Author
Sandip Ghosh, Aishika Majumdar and Sandhita Poddar
---
## 📜 License
This project is licensed under the MIT License.
Feel free to use, modify, and share with proper attribution.