| Issue |
ITM Web Conf.
Volume 54, 2023
2nd International Conference on Advances in Computing, Communication and Security (I3CS-2023)
|
|
|---|---|---|
| Article Number | 01010 | |
| Number of page(s) | 6 | |
| Section | Computing | |
| DOI | https://doi.org/10.1051/itmconf/20235401010 | |
| Published online | 04 July 2023 | |
A Study on Calories Burnt Prediction Using Machine Learning
Jaipur Engineering College & Research Centre, Computer Science Department, Jaipur, Rajasthan, India
1 Punita Panwar: punitapanwar7@gmail.com
In this growing technological era, People are less aware of their health and mental stability. Due to lack of time, they intake more junk food than healthy options, which leads to an increase in the total calorie rate in their body. which is a major cause of obesity a calorie is the rate of energy stored and energy expenditure. People nowadays want quick solutions to every problem they want to exercise less and get more results, so to check the level of improvement and the burnt calories level after exercise in the human body we came up with this machine Learning System which takes some attributes as input and gives approximate calories burnt value which will motivate people to do more exercise and will show their daily growth The project is nourished with more than 15,000 data and its MAE (Mean Absolute error) is 1.48 which will enhance over time for better Results.
Key words: ML(Machine learning) / Kaggle / Collab / XGBOOST / Decision tree / linear Regression / SVR / AdaBoost regressor
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.