I will build loan default prediction model

I will design and implement a robust machine learning model using the XGBoost algorithm to predict loan default risk with an accuracy above 80%. The project demonstrates the application of data-driven techniques to financial decision-making, helping lenders minimize risks and improve credit allocation. By collecting and preprocessing financial datasets, I will engineer features such as credit score, income, employment stability, and repayment patterns to train and validate the model. Special focus will be placed on balancing precision and recall to ensure reliability in identifying true default risks, not just achieving high accuracy. Leveraging XGBoosts gradient boosting capabilities, the model will be optimized for performance, interpretability, and scalability, making it practical for deployment. This work highlights the real-world impact of machine learning in financial services by reducing losses, supporting responsible lending, and enabling smarter, data-driven decisions in risk management.