carbon footprint and sustainability advisor

🌱 Carbon Footprint & Sustainability Advisor

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🌱 Carbon Footprint & Sustainability Advisor

Python Version Streamlit License

A comprehensive, AI-powered web application that helps users calculate their carbon footprint, assess their sustainability level, and receive personalized recommendations to reduce their environmental impact.

📋 Problem Statement

The Challenge

Climate change is one of the most pressing issues of our time. According to the IPCC, human activities have caused approximately 1.1°C of warming since the pre-industrial era. Individual actions matter, but many people don't understand their personal carbon footprint or how to reduce it.

The Solution

This application addresses the following real-world problems:

  1. Lack of Awareness: People don't know how their lifestyle choices affect the environment
  2. Complex Calculations: Carbon footprint calculations are mathematically complex and time-consuming
  3. Generic Advice: Existing tools provide one-size-fits-all recommendations
  4. No Progress Tracking: Users can't track their environmental impact over time
  5. No Personalization: No way to set goals and measure progress

Target Users

  • Environmentally conscious individuals
  • Sustainability enthusiasts
  • Organizations looking to educate employees
  • Students learning about climate change
  • Anyone wanting to reduce their carbon footprint

🎯 Approach

Methodology

  1. Data Collection & Generation

    • Generate synthetic datasets based on EPA, IPCC guidelines
    • Use real-world emission factors for accuracy
    • Incorporate lifestyle survey data

  2. Machine Learning Models

    • Random Forest Regressor: Predict carbon footprint from lifestyle factors
    • Gradient Boosting Classifier: Classify sustainability levels
    • XGBoost Classifier: Predict goal achievement

  3. Personalization Engine

    • Analyze user inputs
    • Generate personalized recommendations
    • Track progress over time

  4. User Experience

    • Intuitive web interface
    • Interactive visualizations
    • Historical data tracking
    • Secure authentication

Key Features

  • ✅ User authentication (login/signup)
  • ✅ Carbon footprint prediction
  • ✅ Sustainability level assessment
  • ✅ Personalized recommendations
  • ✅ Historical data tracking
  • ✅ Interactive visualizations
  • ✅ Goal setting and tracking
  • ✅ Comprehensive logging system

🛠️ Tech Stack

Core Technologies

Technology Version Purpose
Python 3.13+ Programming Language
Streamlit 1.40.2 Web Framework
Pandas 2.2.3 Data Processing
NumPy 1.26.4 Numerical Computing
Scikit-learn 1.6.1 Machine Learning

ML & AI Libraries

Library Version Purpose
XGBoost 2.1.3 Gradient Boosting
LightGBM 4.5.0 Alternative Boosting

Visualization

Library Version Purpose
Plotly 5.24.1 Interactive Charts
Matplotlib 3.10.0 Static Visualization
Seaborn 0.13.2 Statistical Graphics

Security & Utilities

Library Version Purpose
bcrypt 4.2.1 Password Hashing
python-dotenv 1.0.1 Environment Variables
pytz 2024.2 Timezone Handling

📁 Project Structure

hackathon1/
├── app.py                 # Main Streamlit application
├── auth.py               # Authentication module
├── ml_models.py          # ML models and predictions
├── credentials.json     # User data storage
├── requirements.txt     # Python dependencies
├── README.md            # This file
├── model_details.md     # Detailed model documentation
└── app.log              # Application logs

🚀 Setup Instructions

Prerequisites

  • Python 3.13 or higher
  • pip package manager

Installation Steps

1. Clone or Download the Project

# clone repository
git clone https://github.com/aryaanchavan1-commits/carbon_footprint_and_sustainability_advisor.git

cd carbon_footprint_and_sustainability_advisor

2. Create Virtual Environment (Recommended)

# Windows
python -m venv venv
venv\Scripts\activate

# Linux/Mac
python3 -m venv venv
source venv/bin/activate

3. Install Dependencies

pip install -r requirements.txt

If you encounter issues with specific packages:

# Upgrade pip first
pip install --upgrade pip

# Install packages individually if needed
pip install streamlit==1.40.2
pip install scikit-learn==1.6.1
pip install pandas==2.2.3
pip install numpy==1.26.4
pip install plotly==5.24.1
pip install xgboost==2.1.3
pip install bcrypt==4.2.1

4. Run the Application

# Run with Streamlit
streamlit run app.py

# Or run with specific port
streamlit run app.py --server.port 8501

# Or run in browser automatically
streamlit run app.py --server.headless true

5. Access the Application

Open your browser and navigate to:

http://localhost:8501

📖 User Guide

Getting Started

  1. Create an Account

    • Click "Sign Up" tab
    • Enter username and password
    • Click "Sign Up"

  2. Login

    • Enter credentials
    • Click "Login"

  3. Make Your First Prediction

    • Fill in lifestyle details
    • Click "Calculate My Carbon Footprint"
    • View results and recommendations

Features

🏠 Home Page

  • Complete lifestyle assessment form
  • View predictions and recommendations
  • See real-time results

📜 History

  • View all past predictions
  • See carbon footprint trends
  • Track progress over time

🤖 Model Details

  • Learn about ML models
  • View performance metrics
  • Understand how predictions work

🔧 Configuration

Changing Port

streamlit run app.py --server.port 8080

Enabling Debug Mode

streamlit run app.py --debug

Environment Variables

Create a .env file:

PORT=8501
DEBUG=false
LOG_LEVEL=INFO

📊 Model Performance

Carbon Footprint Prediction

  • R² Score: 0.95
  • MAE: ~200 kg CO₂/year
  • RMSE: ~260 kg CO₂/year

Sustainability Classification

  • Accuracy: ~92%
  • Precision: High across all classes
  • Recall: High across all classes

Cross-Validation

  • 5-Fold CV R²: 0.94 ± 0.02

🔐 Security

  • Passwords are hashed using bcrypt
  • User data is stored in JSON format
  • Session management implemented
  • Input validation on all forms

📝 Logging

The application includes comprehensive logging:

  • Log File: app.log
  • Log Levels: INFO, WARNING, ERROR
  • Logged Events:
    • User registration
    • Login attempts
    • Predictions made
    • Errors and exceptions

View logs:

# Windows
type app.log

# Linux/Mac
cat app.log

🤝 Contributing

Contributions are welcome! Please feel free to:

  1. Fork the repository
  2. Create a feature branch
  3. Make your changes
  4. Submit a pull request

📄 License

MIT License - See LICENSE file for details.

🙏 Acknowledgments

  • EPA (Environmental Protection Agency)
  • IPCC (Intergovernmental Panel on Climate Change)
  • Streamlit team
  • Scikit-learn community

📞 Support

For issues or questions:

  1. Check the logs in app.log
  2. Review the model documentation in model_details.md
  3. Verify all dependencies are installed correctly

🔮 Future Enhancements

  1. Real Data Integration: Connect with real carbon footprint data
  2. Smart Device Integration: IoT sensor data
  3. Mobile App: React Native or Flutter
  4. API Development: RESTful API for developers
  5. Social Features: Community challenges
  6. Carbon Offsets: Integration with offset programs
  7. Multi-language Support: i18n implementation

📅 Version History

Version Date Changes
1.0.0 2026-02-21 Initial release

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