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Credit Score Dashboard Development
Project type
Dashboard
Tools
Python, Streamlit, XGBoost, Shapley values library, JSON, Pandas, NumPy, Matplotlib/Seaborn (for visualizations), GitHub
Skills
Web application development, Interactive dashboard creation, Model deployment, Data preprocessing, Handling missing data, Feature importance analysis, User interface design, Communication skills
Developed an interactive Streamlit dashboard for credit score prediction, utilizing a pretrained XGBoost classifier model.
The dashboard takes customer information as input, and is designed to handle missing data. Displays key customer details along with the predicted default probability and credit approval status, providing transparency through Shapley values to highlight the main factors influencing each decision.
Enables dynamic updates, allowing users to modify input data and observe changes in real-time predictions. The model leverages historical banking data, which has been precomputed, ensuring a streamlined and responsive user experience.
