Linear Regression and K Nearest Neighbors Machine Learning Models for Person Fat Forecasting

Abstract

Predicting a person's person fat percentage is an important part of keeping tabs on their health and fitness. An accurate assessment of person fat allows for the development of individualized programmer for health and wellbeing, the promotion of illness prevention, and the evaluation of the efficacy of weight management initiatives. This study reviews the current state of the art in person fat prediction approaches, which includes the use of machine learning algorithms. Obesity is a chronic condition characterized by high levels of person fat and is linked to several health issues. Since several methods exist for estimating person fat percentage to evaluate obesity, these assessments are usually expensive and need specialized equipment. Therefore, determining obesity and its associated disorders requires an accurate estimate of person fat proportion according to readily available person measures. This paper presented a machine-learning model for forecasting person fat. This problem is a regression, so this paper used two regression models to deal with the regression dataset. This paper used linear regression (LR) and k nearest neighbors (KNN). The two models were applied to real datasets. The dataset has 252 records. The results showed the LR has the highest score than the KNN model.