This is a demo on a project I'm workin on related to credit scoring modeling. I serve the model in Streamlit simulating a credit evaluations platform with quick and seamless repsonses for an specific input, based on the credit model performed. The platform leverages a FICO score and the credit historical data (both RGN) to evaluate the probability of default of a potential customer. You can use the tool here and please dont hesitate to reach out if you have any feedback. Online Evaluation
CreditN is designed for a hassle-free experience, offering a predictive evaluation without relying on sociodemographic data. Here's a quick overview of the features:
- Input Form: Use the sidebar to input your name, desired loan term, loan amount, and annual income.
- Submit Button: Click the "Submit" button to initiate the credit assessment process.
- Results: Receive your Credit Ninja Score, probability of default, and other relevant details.
- Input Form: Enter your details in the sidebar, including your name, desired loan term, loan amount, and annual income.
- Submit: Click the "Submit" button to start the credit assessment.
- Results: Explore your Credit Ninja Score, probability of default, and other insights.
- The platform utilizes your FICO score and other key factors for the assessment.
- No sociodemographic data is used, ensuring a privacy-focused evaluation.
To use CreditN:
- Clone this repository.
- Install the required dependencies using
pip install -r requirements.txt. - Run the Streamlit app with
streamlit run app.py.
Feel free to explore, contribute, and make the most of Credit Ninja!
Note: The provided Python code includes functionalities for evaluating credit scores and creating a Streamlit app. Ensure you have the necessary dependencies installed before running the application.