The project focused on leveraging machine learning algorithms to predict credit default risk using a German credit dataset. Through meticulous analysis and model training, the team aimed to enhance financial decision-making by providing accurate predictions on whether credits would default. By harnessing the power of predictive analytics, the project aimed to offer valuable insights to financial institutions, enabling them to mitigate risks effectively and make informed lending decisions. Through rigorous evaluation of various machine learning techniques, the project sought to deliver a robust predictive model capable of identifying potential default cases, thereby contributing to the optimization of credit assessment processes and minimizing financial losses.