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https://github.com/venky-1710/stress-level-predection
Stress Level Prediction is a web app using machine learning to estimate user stress levels. It takes inputs like anxiety, sleep quality, and academic performance, then predicts stress using a Decision Tree Classifier. Built with Python, Flask, and scikit-learn, it's useful for students, researchers, and those interested in stress management.
https://github.com/venky-1710/stress-level-predection
css flask html machine-learning numpy pandas python python-sklearn scikit-learn
Last synced: about 6 hours ago
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Stress Level Prediction is a web app using machine learning to estimate user stress levels. It takes inputs like anxiety, sleep quality, and academic performance, then predicts stress using a Decision Tree Classifier. Built with Python, Flask, and scikit-learn, it's useful for students, researchers, and those interested in stress management.
- Host: GitHub
- URL: https://github.com/venky-1710/stress-level-predection
- Owner: venky-1710
- Created: 2024-06-20T22:02:20.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-07-09T22:04:32.000Z (4 months ago)
- Last Synced: 2024-07-10T02:25:09.778Z (4 months ago)
- Topics: css, flask, html, machine-learning, numpy, pandas, python, python-sklearn, scikit-learn
- Language: HTML
- Homepage:
- Size: 402 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Stress Level Prediction
This project is a web application that predicts stress levels based on various input factors. It uses a machine learning model to make predictions and provides a user-friendly interface for input and result display.
## Features
- User input form for various stress-related factors
- Machine learning model (Decision Tree Classifier) for stress level prediction
- Responsive web design with custom styling
- Input validation to ensure data integrity
- Error handling for invalid inputs
## Technologies Used
- Python
- Flask
- scikit-learn
- pandas
- numpy
- HTML/CSS
- JavaScript
## Project Structure
- `app.py`: Main Flask application file containing the server-side logic and machine learning model
- `templates/`: Directory containing HTML templates
- `login.html`: Input form for user data
- `result.html`: Displays the predicted stress level
- `error.html`: Error page for invalid inputs
- `static/`: Directory for static files
- `styles.css`: Custom CSS styles for the application
- `StressLevelDataset.csv`: Dataset used for training the model (not included in the repository)
## Setup and Running the Application
1. Clone the repository:
```sh
https://github.com/venky-1710/stress-level-predection.git
```
2. Install the required dependencies:
```sh
pip install flask pandas numpy scikit-learn
```
3. Ensure you have the `StressLevelDataset.csv` file in the project root directory.
4. Run the Flask application:
```sh
python app.py
```
5. Open a web browser and navigate to `http://localhost:5000` to use the application.
## How to Use
1. Fill in the form with your stress-related factors. Each field has a specified range of values.
2. Click the "Submit" button to get your predicted stress level.
3. The result page will display your predicted stress level based on the input factors.
## Future Improvements
- Implement user authentication and data storage
- Add more detailed explanations for each input factor
- Incorporate additional machine learning models for comparison
- Develop a feature to track stress levels over time
## Contributing
Contributions to improve the project are welcome. Please feel free to fork the repository and submit pull requests.
## License
This project is open source and available under the [MIT License](LICENSE).