Credit Risk Scoring & AI Dashboard for Fintech Application
Python (pandas, LightGBM, scikit-learn) Figma (UX mockup) Google Colab Developed and deployed a supervised machine learning model (Random Forest / LightGBM) to predict client eligibility for credit in a neobank interface. Cleaned and labeled data from Home Credit’s dataset, engineered relevant features, and applied classification logic for explainable output. Designed a user-facing dashboard using Figma and ensured full GDPR compliance (data anonymization, transparency notice). Contributed to both data preparation and model evaluation phases. The final interface displays a probability score from 0 to 1, aiding advisors in decision-making.