This is a machine learning web application that predicts obesity levels based on user input using a pre-trained model. The app uses FastAPI for the backend and is designed to take multiple user inputs related to health and lifestyle to classify obesity levels.

• Predicts obesity level based on input such as gender, age, height, family history of obesity, eating habits, physical activity, and more.
• Interactive web form built with HTML.
• FastAPI framework for backend logic and serving the machine learning model.
• Random Forest machine learning model for prediction.
• Deployed on a cloud platform (Render, Heroku, etc.).

├── src
│   ├── main.py            # Main FastAPI app
│   ├── data_model.py      # Data model definition
│   ├── model.pkl          # Pre-trained model
│   ├── CAEC_label_encoder.pkl  # Label encoders for categorical features
│   ├── CALC_label_encoder.pkl  # Label encoders for categorical features
│   ├── MTRANS_label_encoder.pkl  # Label encoders for categorical features
│   ├── templates
│       └── form.html      # HTML template for the web form
├── requirements.txt       # List of dependencies
└── README.md              # Project documentation

1. The user inputs their personal and lifestyle data (such as age, gender, physical activity, etc.) into a form on the web app.
2. The backend receives the data, processes it using a pre-trained machine learning model, and returns a prediction of their obesity level.
3. The app provides the result back to the user, displaying it on the same page.

1. Clone the repository:

   git clone https://github.com/YourUsername/Obesity_Prediction.git
   cd Obesity_Prediction

2. Set up a virtual environment:

   python -m venv myenv
   source myenv/bin/activate  # On Windows use `myenv\Scripts\activate`

3. Install the required dependencies:

   pip install -r requirements.txt

4. Run the application:

   uvicorn src.main:app --reload

5. Open the app in your browser:

   Go to http://127.0.0.1:8000 to see the form and predict obesity levels.

• Open the application in your browser.
• Fill in the form with the required information.
• Submit the form, and the app will display your predicted obesity level.

To deploy the application on Render, Heroku, or any cloud service, follow these steps:

1. Add your Procfile (for Heroku):

   web: uvicorn src.main:app --host 0.0.0.0 --port $PORT
   

2. Push to GitHub and link your repository to Render/Heroku.

3. Deploy your app, and the platform will automatically detect the requirements.txt and build the app.

• FastAPI: A modern, fast (high-performance) web framework for building APIs.
• Pandas: Data manipulation and analysis.
• Scikit-learn: For the machine learning model.
• Joblib: To save and load label encoders.
• HTML: Frontend for user interaction.
• Jinja2: Templating engine for HTML in FastAPI.

• The model used is a Random Forest Classifier, trained on a dataset containing lifestyle and personal health information.
• Pre-processing was done to encode categorical variables like CAEC, CALC, MTRANS using label encoders.

• Here's the dataset source: Estimation of Obesity Levels Based On Eating Habits and Physical Condition

1. Main Form:

   

2. Prediction Result:

   

• Add validation for form inputs.
• Improve UI/UX for better usability.
• Explore deploying on additional cloud platforms.
• Add more classification models for comparison.