• Niharikareddy Meenigea

Today, people have very tight schedules due to changes in their lifestyle and work commitments. But it requires regular physical activity to stay fit and healthy. People do not pay attention to their eating habits leading to obesity. Obesity is becoming a big and widespread problem in today's lifestyle. This makes people choose diets and do a lot of exercise to stay fit and healthy. The main thing here is that everyone should have complete knowledge of calories consumed and burned, it is easy to keep track of their calories as it is available on product labels or on the internet. Tracking calories burned is the tricky part because there are so few devices for this. Calories burned by an individual based on the MET chart and formula. The main program of this study is to predict calories burned using a boost XG regression model like an ML (machine learning) algorithm to display accurate results. The model is given more than 15,000 data and its mean absolute error is 2.7. This error will improve over time by providing additional data for the enhanced XG regression model.

Machine Learning, Environment, Data Source, Data Analysis and XGB Regressor.

"Calorie Burn Prediction: A Machine Learning Approach using Physiological and Environmental Factors", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.1, Issue 2, page no.587-595, July-2014, Available :http://www.jetir.org/papers/JETIR1701933.pdf

"Calorie Burn Prediction: A Machine Learning Approach using Physiological and Environmental Factors", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.1, Issue 2, page no. pp587-595, July-2014, Available at : http://www.jetir.org/papers/JETIR1701933.pdf