{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/7b47423cb6414cbe994923da7472c8df\" frameborder=\"0\" width=\"1920\" height=\"1440\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1440,"width":1920,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1440,"thumbnail_width":1920,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/7b47423cb6414cbe994923da7472c8df-28ecdeada60cbeda.gif","duration":292.785,"title":"Feature Selection for Fraud Detection","description":"In 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."}