Obesity Level Detector A machine learning model that estimates obesity levels based on user inputs, including dietary habits and physical activity. Hosted on the web, the model provides personalized health advice and recommendations.
Machine learning project to predict obesity risk levels based on lifestyle and demographic data. This project utilizes advanced algorithms like CatBoost, LightGBM, and more to classify individuals into different obesity categories
This repository contains models that predict the obesity level of patients based on their eating/lifestyle habits and physical condition.
Developed a predictive model to classify individuals into one of seven weight categories (ranging from insufficient weight to obesity type 3) based on various personal factors using diverse neural network architectures.
Multi-class classification task of determining an individual's level (or lack) of obesity, using LightGBM
This website uses a Bayesian Network to diagnose people with obesity and see what would have happened if they made different lifestyle choices
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