video1.0<iframe src="https://www.loom.com/embed/7b47423cb6414cbe994923da7472c8df" frameborder="0" width="1920" height="1440" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>14401920Loomhttps://www.loom.com14401920https://cdn.loom.com/sessions/thumbnails/7b47423cb6414cbe994923da7472c8df-28ecdeada60cbeda.gif292.785In this video, I walk you through the process of feature selection using regression techniques from scikit-learn to identify important predictors for fraud detection. I discuss the significance of various features, such as product title and item category, and how they relate to predictive power. I also recommend experimenting with different subsets of features to optimize model performance. Please take note of the suggested approaches and consider applying them in your own analyses.