Insights Unveiled: Leveraging Data to Drive Strategic Decisions in Loan Default Prediction

My project addresses a critical need in the financial sector by assisting financial institutions in predicting loan defaults and refining prediction models to optimize credit policy.

Key insights reveal:

1. Challenges with data imbalance and the need for relevant data for efficient predictive models
2. Highlight the effectiveness of a decision tree model in predicting both true positives and true negatives, in addition to the false positives and false negatives.

My efforts have driven a deeper appreciation within management for the significance of accurate data in shaping credit policies and enhancing decision-making processes.

Your feedback is welcome to further improve this Capstone project and Python skills. Thank you for your time. Happy Coding.