video1.0<iframe src="https://www.loom.com/embed/1a512cbc33474dfc8dc77e5aca241b75" frameborder="0" width="1920" height="1440" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>14401920Loomhttps://www.loom.com14401920https://cdn.loom.com/sessions/thumbnails/1a512cbc33474dfc8dc77e5aca241b75-e454cb2b4ce147ae.gif522.673In this video, I discuss the process of building a fraud detection model for PayPal users. I outline two primary tasks: identifying frosters based on user profiles and operationalizing the model effectively. I emphasize the importance of data exploration, preparation, and feature engineering, while also addressing the challenges of imbalanced data. Please take note of the strategies I suggest for model evaluation and operationalization, as your input will be valuable in refining our approach.