video1.0<iframe src="https://www.loom.com/embed/0485dcd5917944cea6c63072f3558c5c" frameborder="0" width="1920" height="1440" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>14401920Loomhttps://www.loom.com14401920https://cdn.loom.com/sessions/thumbnails/0485dcd5917944cea6c63072f3558c5c-34cca850588d7a37.gif114.886In this video, I walk you through the steps to build a fraud model aimed at reducing fraudulent consumer accounts for DoorDash. We will be using a provided CSV file containing historical data, which includes a fraud indicator and various aggregated variables. I outline the necessary steps, including exploratory data analysis, data preparation, feature extraction, and model selection. Please make sure to follow these steps closely as you work on the project.