Happiness Index Predictor

This project was developed during a 24-hour hackathon and aims to predict the happiness index of users through two approaches: manual questionnaire responses and facial image recognition. The application provides personalized recommendations to uplift the user's happiness index and mood based on the predictions.

Features

1. Manual Questionnaire Approach:

• Users can fill out a questionnaire covering various aspects of life such as work, relationships, health, etc.
• Machine learning models trained on global happiness index data analyze the responses to predict the user's happiness index.
• Hardcoded recommendations are provided based on the predicted happiness index to improve the user's happiness in different life aspects.

2. Facial Image Recognition Approach:

• Users answer personal questions such as hobbies and coping mechanisms for sadness.
• Users upload a facial image, and deep learning models recognize the user's mood.
• Recommendations are given based on the predicted mood to uplift the user's mood, such as suggesting activities or relaxation techniques.

Instructions to Run

1. Clone the repository:

   git clone https://github.com/your-username/happiness-index-predictor.git

2. Navigate to the project directory:

   cd happiness-index-predictor

3. Install the required dependencies:

   pip install -r requirements.txt

4. Run the Streamlit application:

   streamlit run app.py

5. Open your web browser and go to http://localhost:8501 to access the application.

Technologies Used

• Python
• Streamlit
• Machine Learning (scikit-learn)
• Deep Learning (TensorFlow, Keras)

Contributing

Contributions are welcome! Please fork the repository and submit a pull request with your improvements.

License

This project is licensed under the MIT License.