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@article{188498,
        author = {Vinod G and Vitesh Babu M and Vikas Gowda and Shubham Deep and Sathvik M},
        title = {LiveLight CarbonFootprint: An AI-Enabled Machine Learning Framework for Personalized Emission Awareness and Sustainability Guidance},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {2345-2350},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=188498},
        abstract = {Escalating greenhouse gas emissions and the rapid growth of global carbon footprints have intensified the need for accessible, data-driven tools that help individuals understand and reduce their environmental impact. This work presents LiveLight CarbonFootprint, a web-based system that leverages supervised machine learning, interactive visualization, and an AI-driven chatbot to estimate monthly personal carbon emissions from lifestyle data such as transportation, energy usage, diet, and waste habits. The framework employs an MLPRegressor model with standardized input features to generate personalized carbon footprint predictions, complemented by category-wise breakdowns, actionable recommendations, and downloadable PDF reports. The system further integrates a generative AI assistant to offer real-time sustainability advice, enhancing user engagement and awareness. Experimental evaluation demonstrates accurate predictions, low response times, and positive user acceptance, indicating that the proposed solution can effectively bridge the gap between high-level climate goals and everyday behavioral change.},
        keywords = {Carbon Footprint, Machine Learning, Sustainability, Streamlit, Generative AI, MLPRegressor, Environmental Informatics.},
        month = {December},
        }