Published Paper Details:

In this study, there is exploration of how people's BMI changed pre and post the onset of covid pandemic, considering factors like food habits (the nutritious value of what they eat) and physical activities. They not just eyeballing the data--system using super-smart computer techniques called Reinforcement Learning, specifically Deep Q Network and Random Forest Regression and Gradient Boost Regression. Before COVID-19, know people had certain eating food habits and lifestyle habits. Now, with the pandemic, those might have changed. Using Deep Q Network, our computer system learns from this data and figures out how these changes are linked to BMI. It's like teaching a computer to understand the consequences of different habits on weight. Gradient Boost Regression is another technique being used. It helps the computer learn not just from the data have but also by exploring possibilities like, what if someone changed their eating habits or exercise routines? This way, system not just looking at what happened but also predicting what could happen. By combining these techniques, study aim to unravel how food choices and physical activities during and after Covid-19 might have influenced BMI. It's like having a smart assistant to help us understand the connection between lifestyle changes and weight, shedding light on how they can stay healthy in this challenging time.