The global carbon footprint has surpassed 40 billion tons annually, highlighting the urgent need for climate action. EcoTrack is designed to empower individuals to measure and understand their monthly carbon footprint, providing personalized insights and actionable recommendations to help reduce environmental impact. By promoting eco-conscious choices, EcoTrack aims to foster a responsible and sustainable global community.
- Utilized Pandas for data manipulation and analysis, enabling efficient handling of datasets.
- Employed NumPy for numerical operations and array manipulation to enhance computational performance.
- Integrated scikit-learn to implement machine learning algorithms, driving data-driven insights.
- Set up the frontend using Streamlit to create a responsive and interactive web application.
- Developed an intuitive UI with Streamlit components, CSS, and JavaScript for a seamless user experience.
- Conducted comprehensive testing to ensure that backend and frontend components operate seamlessly together.
- Clone the repository and navigate to the project directory.
- Install dependencies by running:
pip install -r requirements.txt
- Launch the application by running:
streamlit run app.py
EcoTrack is built to inspire and empower individuals to reduce their carbon footprint and contribute to a healthier planet.