video1.0<iframe src="https://www.loom.com/embed/79cb485e56d34676a152ce4b49ad4253" frameborder="0" width="1920" height="1440" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>14401920Loomhttps://www.loom.com14401920https://cdn.loom.com/sessions/thumbnails/79cb485e56d34676a152ce4b49ad4253-abe69f328e6cf254.gif356.805In this video, I discuss the process of model selection and hyperparameter tuning, specifically using XGBoost for fraud detection. I highlight the importance of parameter tuning and the strategies I employed, such as random grid search and down-sampling techniques to address class imbalance. I also share the AUC results from my experiments, which show improvements in both validation and test sets. Please take a moment to review the findings and let me know your thoughts on the model's performance.