I'm a bit late to the party! 2 years since the videos were released and they're still proving to be massively helpful. I've enjoyed all the videos in the series so far. Most of all, the extra information you provide is priceless. Thanks a bunch. It's so useful to see you stepping through the process.

Your work is awesome.. thank you Abhishek

Thanks for this initiative.Gaining a lot Abhishek.

learn lot From you thanks for this playlist

Great bro

great video. What does the step_1.b.check() thing do ? is it a function you defined ?

Hello, I am curious would you be able to explain how you record your videos (Screen recording &camera)? It seems that you can switch between computer screen and iPad and am wondering what software you use

Sir, for a classifier model , can we apply encoder to categorical variables or is it like only we can apply to a regressor model?, also if we have only few features , is it feasible to apply encoder in classifier models?

Sir my question is in categorical variables if we have to use LabelEncoder and there are bad columns like in the exercise then how can we use it in pipeline?? Can you do it in your video part??

Hello , how to make column selection step as part of pipe line. i.e.. if I have trained on 10 columns before performing any transformation , i would like to select only those columns. and make this part of pipeline.. Tried using columnTransformer. but columns Transformed return only the numpy array - so the following step of numeric_transformation and categorical _transformation are breaking

Would it have been worth checking the MAE of the GradientBoostingRegressor model on it's own, to see whether it was lower than the MAE of the average of the two models? Thanks for all the videos.

Hii sir , what is the significance of making avearge of the predictions ?

In of your previous videos we created a custom imputer where we made the model predict the null values...if we are using Pipelines, is it possible to use such custom functions as imputers?