Predicting Body Fat Percentage: A Machine Learning Approach

DHEIVER SANTOS

doi:10.20944/preprints202310.0929.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 15 October 2023 / Approved: 16 October 2023 / Online: 16 October 2023 (07:55:25 CEST)

Accurate estimation of body fat percentage is essential for various health and fitness applications. Traditional methods for measuring body fat, such as underwater weighing, can be costly and inconvenient. In this study, we apply machine learning techniques to predict body fat percentage using easily accessible body measurements and data. The results highlight the effectiveness of regression models in estimating body fat, with Linear Regression, Ridge Regression, and Bayesian Ridge models achieving R-squared scores of approximately 74%, 73%, and 73%, respectively. These models provide a practical and cost-effective solution for individuals and professionals seeking reliable body fat estimates.

body fat percentage; machine learning; regression models; feature engineering; outlier detection; data preprocessing; health and fitness; predictive modeling

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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Predicting Body Fat Percentage: A Machine Learning Approach

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