{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/43e6edd4de6c453baca9476f5c14e999\" 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/43e6edd4de6c453baca9476f5c14e999-499ee3579d3a2e54.gif","duration":448.6,"title":"Feature Selection","description":"In this video, I discuss my approach to feature selection, focusing on identifying important features while managing collinearity. I utilize a two-step process involving feature importance and hierarchical clustering to optimize the feature set. I also share insights on encoding techniques and the importance of preserving certain features for model accuracy. Please take a look at the solution file for the code details, and I would appreciate your feedback on this approach."}