A machine learning pipeline to classify obesity levels from structured health and habit data using Python, scikit-learn, and Streamlit.
WellnessWise_ml model is neural network machine learning model trained for Providing health risk for diseases like Cardiovascular, Hypertension, cancer, Diabetes, and obesity. There are two types of model one for h5, other is in tensor flow lite formate for Mobile phone integration and low powered device's.
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.
Android app that predicts chronic disease risk such as diabetes, cancer, obesity, cardiovascular diseases based on user health data, written in kotlin and jetpack compose. Virtual University Final Year Project.
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 project predicts obesity levels using machine learning based on lifestyle and health data. The model, built with **Random Forest**, classifies individuals into categories like Normal Weight and Obesity. It aims to assist healthcare professionals in identifying obesity risks.
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.
This website uses a Bayesian Network to diagnose people with obesity and see what would have happened if they made different lifestyle choices
Obesity Prediction Using Machine learning
Machine learning implementation for obesity risk stratification and predictive analysis of biological/lifestyle markers.
A machine learning pipeline for obesity prediction using TabPFN and Hydra.
Predicting obesity from NHANES medical data with three compared models (tree, random forest, XGBoost) in R / Prédiction de l'obésité à partir de données NHANES avec trois modèles comparés en R
The Obesity Prediction Dataset provides a comprehensive collection of attributes related to individuals demographics, lifestyle habits, and health indicators.
A data science project analyzing the Obesity dataset to classify individuals into obesity categories using a Decision Tree model. Includes preprocessing, visualization, and model evaluation.
Predicting obesity risk levels using multi-class Logistic Regression (OvA & OvO) with an end-to-end ML pipeline on the UCI Obesity dataset.
Multi-class classification task of determining an individual's level (or lack) of obesity, using LightGBM
This project leverages Machine Learning to classify individuals into obesity categories—Underweight, Normal, Overweight, or Obesity—based on demographic and lifestyle data. Using a Random Forest Classifier trained on features like age, sex, eating habits, physical activity, and daily routines, the model predicts obesity status with accuracy (86%)
Machine Learning based obesity risk prediction system that analyzes health and lifestyle factors to classify obesity levels and support preventive healthcare decisions.
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