CALORIE BURN PREDICTION
| dc.contributor.author | MIHOUBI, Hadil | |
| dc.contributor.author | SAOUDI, Racha | |
| dc.contributor.author | Supervisor: Hemmak, Allaoua | |
| dc.date.accessioned | 2023-07-03T10:02:26Z | |
| dc.date.available | 2023-07-03T10:02:26Z | |
| dc.date.issued | 2023-06-10 | |
| dc.description.abstract | Life is all about finding balance. and that's most important when it comes to our body. However, staying fit and healthy necessitates frequent physical activity. The variety of burned energy in daily life is directly related to weight maintenance, weight gain, or weight loss. people need to know how many calories they burned each day. Our project is predicting the calorie burned during the workout with the use of machine learning algorithm XGBoost regressor model approach to produce accurate results. the model is fed with more than 15000 data and its mean absolute error is 1.48. Therefore, we built a mobile application that help the users easily by put their values obtain results of burned calorie. | en_US |
| dc.identifier.uri | http://dspace.univ-msila.dz:8080//xmlui/handle/123456789/39923 | |
| dc.language.iso | en | en_US |
| dc.publisher | University of M'sila | en_US |
| dc.subject | XGBoost regressor, machine learning, accurate. | en_US |
| dc.title | CALORIE BURN PREDICTION | en_US |
| dc.type | Thesis | en_US |
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