Fannie May Default Rate Predictions

Working as part of a dedicated team of four students, I engaged in an exciting student project focused on identifying the factors that influence the default rate in mortgage loans from Fannie Mae. Leveraging our collective expertise, we employed a comprehensive approach that incorporated logistic regression, Tableau visualization, and machine learning techniques within SQL. Through meticulous data analysis and model development, we gained valuable insights into the complex dynamics behind loan defaults. Our project not only honed our skills in statistical analysis and data visualization but also provided a practical understanding of the mortgage industry. By successfully unraveling the factors impacting default rates, our work contributed to the broader conversation on risk management in the lending sector.