Credity

a web-based credit card rating model that predicts the creditworthiness of applicants based on their submitted information. This project aims to provide a user-friendly interface for users to input their data, receive a credit rating prediction, and view the result conveniently.

Model architecture

How to Run

To run the Flask app locally, follow these steps:

1. Install virtualenv:

   pip install virtualenv

2. Create a Python virtual environment:

   virtualenv venv

3. Activate the virtual environment:

   • Windows:

     venv\Scripts\activate

   • Linux:

     source ./venv/bin/activate

4. Install the required dependencies using pip:

   pip install -r requirements.txt

5. Start the Flask app:

   python app.py

6. Open your web browser and go to http://localhost:5000 to view the website.

Model Training

The train_model.py file contains the code for training the machine learning model used in the application. We have used the RandomForest algorithm for classification

Referencing Documentation

You can refer to the documentation.docx file located in the project directory for additional documentation and information.

Website Screenshots

MAIN SECTION

WHY SECTION

PARAMETERS SECTION

PREDICTOR SECTION

PREDICTOR SECTION

PREDICTOR SECTION

HELP SECTION

Additional Notes

• Ensure that you have Python and pip installed on your machine.
• Customize the Flask app according to your specific requirements.

License

This project is licensed under the MIT License.