This project develops a predictive model and decision support system (DSS) that evaluates the risk of Home Equity Line of Credit (HELOC) applications. Aimed at assisting sales representatives in banks or credit card companies, this system provides a robust framework for making informed decisions on accepting or rejecting applications. For more detailed information about the dataset and data descriptions, visit the FICO Explainable Machine Learning Challenge.
- Predictive Modeling: Implementation of multiple models to assess the credit risk associated with HELOC applications.
- Comparison and Selection: Analysis and comparison of model performances to select the most effective model for deployment.
- Interactive Interface: A prototype of an interactive interface designed for end-users, enabling clear visualization and easy understanding of predictive outcomes.
- Explanation of Predictions: Tools and methodologies integrated into the interface to explain the basis of predictions, making it transparent and understandable for users.
To explore the interactive interface and see the decision support system in action, visit the live application here:
This link provides direct access to my web-based application developed using Streamlit, allowing you to interact with the predictive models and understand the decision-making process in real-time.