🧬 Keep In Shape: Multiclass Prediction of Obesity Risk 🩺

Behind every piece of data, a type of obesity. Behind the model, a real solution.

Welcome to Keep In Shape, an application that uses multiclass ML techniques to predict people's obesity risk based on lifestyle and eating habits data. This tool is designed to help healthcare professionals and users interested in monitoring and managing obesity-related risks.

DeepWiki

📚 table of contents

1. 🧠 Intelligence for Health
2. 🔍 Project Background
3. 💻 Key Features
4. ⚙️ Tech Stack
5. 📁 Project Structure
6. 🚀 Installation & Usage
7. 📊 About our model
8. 👥 Development Team
9. 🤝 Contributing

🧠 Intelligence for Health

Check out the demo of our app

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🔍 Project Background

Details about:

• Exploratory Data Analysis
• Algorithm experimentation XGBoost
• Model evaluation metrics
• Development of the web application
• Please visit our technical report for a detailed overview of the project.

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💻 Key Features

• Interactive dashboard
• User scenario simulator
• Personalized health recommendations
• Persistent prediction history (supabase)
• Intuitive user interface
• Dockerized deployment

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⚙️ Tech Stack

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Project structure

multiclass_prediction_obesity_risk/
├── client/                      # User interface
│   └── media/                   # Static assets (images, etc.)
├── data/                        # Project data
│   ├── raw/                     # Original dataset
│   └── processed/               # Processed dataset
├── docs/                        # Project documentation
├── server/                      # Backend and model logic
│   ├── model/                   # Trained models and utilities
│   └── utils/                   # Helper functions
├── tests/                       # Automated tests
├── .gitignore                   # Files ignored by Git
├── pyproject.toml               # Project configuration
├── requirements.txt             # Project dependencies
├── README.md                    # Main documentation
└── Dockerfile                   # Containerization setup

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🚀 Installation & Usage

1️⃣ Clone the Repository

git clone https://github.com/fergarcat/multiclass_prediction_obesity_risk.git
cd multiclass_prediction_obesity_risk

2️⃣ Create and Activate a Virtual Environment

python3 -m venv .venv

macOS/Linux:

source .venv/bin/activate

Windows:

.venv\Scripts\activate

3️⃣ Install Dependencies

pip install -r requirements.txt

💡 TIP:
Use pip list to see all installed dependencies.

4️⃣ Set Up Environment Variables

Duplicate the env_example file, rename it to .env

5️⃣ Run the Dashboard

python run_client.py

6️⃣ 🚀 Deploy with Docker

Run the following command to build and start the containers:

docker-compose up --build

7️⃣ Run test

python -m unittest discover tests

📊 About our model

We compared several models to determine which one performed best. You can see the results in this notebook.

Model	Accuracy	Precision	Recall	F1-Score	Train Time (s)	Overfitting
XGBoost	0.9073	0.9070	0.9073	0.9071	2.4346	0.0769
CatBoost	0.9061	0.9056	0.9061	0.9058	35.4323	0.0483
LightGBM	0.9051	0.9048	0.9051	0.9049	4.7564	0.0711
Random Forest	0.9037	0.9030	0.9037	0.9031	3.8831	0.0962
SVM (RBF)	0.8832	0.8822	0.8832	0.8826	5.1034	0.0137
SVM (Linear)	0.8697	0.8684	0.8697	0.8688	5.1625	-0.0029
Logistic Regression	0.8639	0.8624	0.8639	0.8629	0.7557	-0.0013
Decision Tree	0.8408	0.8404	0.8408	0.8404	0.1659	0.1591
KNN	0.7936	0.7906	0.7936	0.7908	0.2451	0.0574
Naive Bayes	0.7755	0.7710	0.7755	0.7691	0.0260	-0.0024

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We identified these two models as optimal.

Model	Accuracy	Precision	Recall	F1-Score	Train Time (s)	Overfitting
XGBoost (Adjusted)	0.9053	0.9048	0.9053	0.9050	5.4682	0.0097
Logistic Regression (Adjusted)	0.8639	0.8639	0.8639	0.8633	0.7708	0.0002

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Finally, we retrained the fastest model to reduce overfitting.

Model	Accuracy	Precision	Recall	F1-Score	Train Time (s)	Overfitting
XGBoost + Optuna	0.9118	0.9117	0.9118	0.9116	4.1328	0.0185

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👥 Development Team

Name	GitHub
Fernando García Catalán	fergarcat
Anca Bacria	a-bac-0
Omar Lengua	Omarlsant
Abigail Masapanta	abbyenredes

🤝 Contributing

Contributions are welcome! To contribute:

1. Fork this repository.

2. Create a new branch:

   git checkout -b feature/new-feature

3. Make your changes and commit them:

   git commit -m "Add new feature"

4. Submit a pull request 🚀

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🚀 Thank You for Using Keep In Shape!

If you have any questions, feel free to open an issue in the repository or contact us.