Customer Risk Credit Model

Introduction

The Customer Risk Credit model is designed to evaluate and predict credit risk using various data inputs. It utilizes the Joblib library for model deployment and is maintained on the Hugging Face platform.

Architecture

The model is built using a Streamlit application, allowing for an interactive user interface to input data and view predictions. It is configured with a color scheme transitioning from blue to green and uses Streamlit version 1.10.0.

Training

The training details of the model are not explicitly provided in the README. However, it typically involves using historical credit data to train a predictive model that can assess the risk associated with new credit applicants.

Guide: Running Locally

To run the Customer Risk Credit model locally, follow these steps:

1. Clone the repository from Hugging Face.
2. Ensure you have Python installed along with the necessary libraries, including Streamlit and Joblib.
3. Run the Streamlit app using the command:

   streamlit run app.py
   

4. Access the local server URL provided in the terminal to interact with the application.

Cloud GPUs

For more demanding computations or larger datasets, consider using cloud-based GPU services such as AWS EC2, Google Cloud Platform, or Azure to enhance performance.

License

The model is licensed under the Creative Commons Attribution 3.0 (cc-by-3.0) license, allowing users to share and adapt the material as long as appropriate credit is given.