This project leverages machine learning to predict obesity levels in individuals based on a set of survey responses about their eating habits and physical conditions. It's designed to raise awareness about obesity and help users better understand their health status.
🚀 Live Website: Visit Obesity Estimator
📊 Kaggle Notebook: Explore the Kaggle Notebook
🔗 Kaggle Live Link: View on Kaggle
Built with Flask and styled using Tailwind CSS, this web application is simple and intuitive, allowing users to fill in a quick survey and receive a prediction of their obesity level along with personalized medical advice.
- Real-Time Prediction: Submit your survey and receive instant feedback on your obesity level.
- Accurate ML Model: Powered by a model trained on an enhanced dataset of over 20k entries with 90%+ accuracy.
- Fun Facts & Data Insights: Learn interesting facts about obesity as you interact with the tool.
- Python & Flask for backend
- Tailwind CSS for modern UI design
- Machine Learning model built using scikit-learn
- Clone the repo:
https://github.com/izik-adio/obesity-dectector.git - Install dependencies:
pip install -r requirements.txt - Run the app:
flask run
The model is based on a dataset of individuals from Mexico, Peru, and Colombia, processed to predict obesity levels with high accuracy. The notebook detailing model creation and insights is available on Kaggle.