Climate change is one of the most pressing challenges of our time, with profound consequences for the environment, economy, and society. Human activities, particularly the burning of fossil fuels, deforestation, and industrial processes, have significantly contributed to the increasing levels of greenhouse gases (GHGs) in the atmosphere. These activities have led to global warming, rising sea levels, and extreme weather conditions. In this context, it is essential to raise awareness and encourage individual contributions toward mitigating climate change.
The Green Shift - Carbon Footprint Tracker with Climate Change Insights project aims to empower individuals to take actionable steps by providing them with a platform to track their personal carbon footprints. It bridges the gap between individual awareness and tangible environmental impact by enabling users to visualize their contributions to carbon emissions. By using machine learning models, the system predicts future carbon footprints and offers personalized recommendations for adopting sustainable habits. Furthermore, real-time integration with climate API Climate Track which ensures the delivery of accurate and insightful data. This initiative not only highlights the importance of personal accountability but also serves as a stepping stone toward a more sustainable future.
• Programming Language: Python (for backend and machine learning models).
• Framework: Streamlit for user interface development.
• Libraries: Pandas, NumPy (data processing), Matplotlib, Seaborn (visualizations).
• Machine Learning Models: Linear Regression, ARIMA/LSTM for time series analysis, and K-Means Clustering.
• Dynamic Insights: Real-time integration of climate data offers users a unique perspective on global and local trends.
• Predictive Analytics: Advanced machine learning algorithms forecast future scenarios, motivating proactive behavior.
• Behavioral Customization: Personalized recommendations adapt to individual lifestyles, ensuring higher user engagement.
A user inputs details: “Drives 50 km daily (petrol), consumes 10 kWh of electricity (non-renewable), and eats meat 5 times a week.”
Output: The platform calculates their total emissions, categorizes them as “Moderate Impact,” and suggests using public transport, adopting a plant-based diet, and installing solar panels.
Visualization: A graph displays their current emissions, projected reductions from recommendations, and comparisons with average footprints globally an in India.
The Green Shift project bridges the gap between awareness and actionable insights by combining user-friendly design with robust machine learning models. Its modular approach ensures scalability and adaptability, making it a comprehensive tool for addressing individual contributions to climate change.
cd Green Shift - Carbon FootPrint Tracker\app
streamlit run index.py
Use latest versions of python libraries mentioned in requirement.txt file. Built with Streamlit
Vivek Kumar Singh LinkedIn