CARBON EMISSION ANALYSIS FOR ECO-FRIENDLY DECISION MAKING
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Ensuring environmental sustainability and mitigating climate change requires a deep understanding of our carbon footprint. Machine learning has emerged as an essential tool in this endeavor, allowing us to accurately predict individual emissions based on various lifestyle factors across different demographic categories. Our framework is specifically designed for this purpose - harnessing the power of deep learning to develop a comprehensive system that can analyze 12 categorical features related to behavior and consumption patterns such as diet, transportation habits, energy usage, and waste management practices. By employing ensemble learning with top performing models like Random Forests, Gradient Boosting, XGBoost, and CatBoost at its core; we are able to achieve accurate predictions. to
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Niteesh, M. S., Darshini, G. & Choudhry, M. D. (2026). Carbon Emission Analysis for Eco-Friendly Decision Making. International Journal of Science, Strategic Management and Technology, 02(04). https://doi.org/10.55041/ijsmt.v2i4.321
Niteesh, Malle, et al.. "Carbon Emission Analysis for Eco-Friendly Decision Making." International Journal of Science, Strategic Management and Technology, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i4.321.
Niteesh, Malle,Gandi Darshini, and Mani Choudhry. "Carbon Emission Analysis for Eco-Friendly Decision Making." International Journal of Science, Strategic Management and Technology 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i4.321.
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