Predicting stress levels based on sleep pattern features using machine learning.

• Description
• Installation
• Usage
• Technologies
• Features
• License
• Acknowledgements

This project is a web application that predicts stress levels during sleep based on various sleep pattern features. It utilizes machine learning models to make predictions based on input data provided by the user. The web app is built using Flask for the backend and HTML/CSS for the frontend.

To run the project locally, follow these steps:

1. Clone the repository:

• git clone https://github.com/your-username/stress-level-prediction.git

2. Navigate to the project directory:

• cd stress-level-prediction

3. Install the required dependencies:

• pip install -r requirements.txt

1. Start the web app:

• python app.py

2. Open your web browser and navigate to:

• http://localhost:5000

3. Enter the sleep pattern features in the provided form and click "Predict" to get the stress level prediction.

• Python
• Flask
• HTML
• CSS
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Scikit-learn

• User-friendly web interface to input sleep pattern features.
• Machine learning models for stress level prediction.
• Visualization of data insights and model performance metrics.
• Responsive design for different screen sizes.

This project is licensed under the MIT License.

The dataset used in this project is obtained from source.