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:

   https://github.com/venky-1710/stress-level-predection.git

2. Install the required dependencies:

   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:

   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.