• Mrs. Sneha Gokarnkar
• Dr. Sangeetha G

The pandemic has dramatically altered daily routines worldwide, with increased dependence on electronic gadgets for work, education, and entertainment. Work from home culture increased during the pandemic and persisted even after the pandemic. Though the GHG (Greenhouse Gas) emission is reduced in other sectors, it was predicted that this has increased in housing sectors during the pandemic. Our study aims to investigate the impact of increased electronic gadget usage on household carbon emissions during the pandemic. Through a combination of surveys, energy consumption data analysis, and carbon footprint assessments, we examine changes in electronic device usage patterns and their subsequent environmental consequences. Carbon emissions associated with these changes were quantified, considering factors like the number of device types, number of users, and energy consumption. The proposed study highlights the impact of electronic gadget usage on household carbon emissions during the pandemic. For the prediction of household carbon emissions supervised machine learning technique is used. Supervised machine learning is a digital technology that advances digital systems and processes and is categorized under artificial intelligence. Supervised machine learning is a type of machine learning where the algorithm is trained on a labeled dataset, mapping inputs to outputs by generalizing patterns from the training data and making predictions or decisions based on data. The findings of this research contribute valuable insights into the environmental implications of the widespread use of electronic gadgets during the pandemic. Understanding these impacts is essential for individuals, policymakers, home business makers, and technology developers to formulate strategies that promote sustainable gadget usage, minimize carbon footprints, and contribute to a more environmentally conscious post-pandemic society.

Green Computing, Greenhouse Gas (GHG), pandemic, Supervised Machine Learning, Regression Analysis. COVID-19.

"Prediction of Household Carbon Emission During Pandemic Using Machine Learning Model", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 2, page no.22-31, February-2024, Available :http://www.jetir.org/papers/JETIRGC06003.pdf

"Prediction of Household Carbon Emission During Pandemic Using Machine Learning Model", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 2, page no. pp22-31, February-2024, Available at : http://www.jetir.org/papers/JETIRGC06003.pdf