International Journal of Empirical Research Methods

ISSN NO : 2995-6110

The International Journal of Empirical Research Methods is dedicated to promoting and advancing empirical research methodologies across various disciplines. This journal aims to provide a platform for researchers, scholars, educators, and practitioners to share insights, methodologies, and findings that contribute to the improvement of empirical research practices and the enhancement of evidence-based decision-making.

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CALORIES BURNT PREDICTION USING MACHINE LEARNING APPROACH

1 Rangamati Science And Technology University,,Bangladesh

Volume 1

Issue 1

Page: [29 - 39]

Published Online: October 23, 2023

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  • Abstract

    Calorie Burnt Prediction By Machine Learning Algorithm” Aim To Predict The Number Of Calories Burnt By An Individual During Physical Activity Using Machine Learning Techniques. We Collected A Dataset That Includes Features Such As Heart Rate, Body Temperature, And Duration Of Activity. We Used Various Machine Learning Models, Including XGBoost, Linear Regression, SVM And Random Forest, To Predict Calorie Burn Based On 15,000 Records With Seven Features. The Results Indicate That The XGBboost Model Can Accurately Predict Calorie Burn With A Minimum Mean Absolute Error Of Calories. This Work Contributes To The Growing Body Of Research On Using Machine Learning For Health And Fitness Applications And Has Potential Implications For Personalized Health Coaching And Wellness Tracking. The Highest Accuracy Of Training And Testing Is Gained By The XGBboost Model With 99.67% With Mean Absolute Error Is Almost 1.48%.

    Keywords: Machine Learning Algorithm Calories Health And Fitness Applications XGBboost Model

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  • References

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