This project predicts and analyzes the environmental impact of consumer products using:
- Machine learning models (Random Forest and XGBoost)
- Real-time Amazon product scraping
- A Chrome extension overlay
- A web-based React interface
| Folder | Purpose |
|---|---|
backend/ |
Flask backend and machine learning API |
frontend/website/ |
React website (Home, Learn, Predict) |
frontend/extension/ |
Chrome extension (popup-based estimator) |
common/data/ |
Datasets and shared CSV resources |
ml_model/ |
Trained ML models and encoders |
scrapers/ |
Amazon product scraper (Selenium + ChromeDriver) |
# Clone the repository
git clone <your-repo-url>
cd DSP/
# Create and activate a virtual environment
python -m venv .venv
# On macOS/Linux:
source .venv/bin/activate
# On Windows:
.venv\Scripts\activate
# Install Python dependencies
pip install -r requirements.txt
# Run the Flask app
python -m backend.api.app