video1.0<iframe src="https://www.loom.com/embed/f279af6c56854f3fad1a082d20f252af" frameborder="0" width="1920" height="1440" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>14401920Loomhttps://www.loom.com14401920https://cdn.loom.com/sessions/thumbnails/f279af6c56854f3fad1a082d20f252af-2eb7dee02f2146db.gif555.174In this video, I walk you through my process for selecting and evaluating a model using XGBoost classification. I discuss the importance of AUC scores, especially in the context of class imbalance, and share my findings from tuning parameters and feature selection. I found that using all features improved our model's performance significantly, achieving a test score of 0.9815. Please review the details and let me know your thoughts on the parameter tuning strategies I employed.