Feature Engineering Techniques For Machine Learning in Python

[@GregHogg]

Take my courses at mlnow.ai/!

[@roblee3667]

Run a heat map for all columns when viewing correlations before running PCA, there are way more opportunities for dimensionality reduction.

[@GregHogg]

Oh interesting I should look into this

[@arsheyajain7055]

Been waiting for this one!! Amazing video! Thanks Greg

[@GregHogg]

Glad to hear it! No problem 👍😊👍

[@Eizengoldt]

Greg, your videos are absolutely lovely and reinforce everything I’m learning in my classes, thank you so much

[@GregHogg]

Oh I'm so glad to hear that! Thank you for the support and for the kind words, I really appreciate it :)

[@e_hossam96]

for info: if you delete the column island then you should delete the rows containing value 1 as well or you will have the other encoded columns equals zero in all.

[@rafik1968]

Thank you a lot for these helpful experiments .. it gave me a lot of ideas in data preprocessing !

[@GregHogg]

Super glad to hear it!

[@MohSmadi-c2m]

Thank you so much. Your efforts are really appreciated.

[@gpsvlogs7853]

You're Gold !! Keep up the good work.

[@GregHogg]

Thanks so much really appreciate that

[@kashishrajput4934]

Thank you for posting this, i like all of your videos :)

[@GregHogg]

Great to hear!!

[@russelllapua4904]

Wouldn't this be called Transformation techniques of preprocessing instead? I thought Dimensionality Reduction would be separate from Feature Engineering, with Feature Scaling making up the 3rd subtopic. So something like: Dimensionality Reduction (removing features) PCA Clustering Feature Engineering (creating/transforming features) One-Hot Binning Feature Scaling (normalisation/standardisation of features) Your scaling

[@shivanshusharma8154]

This is very informative....

[@imveryhungry112]

Thank you I wish i was as smart as you ughhh but at least i learned some from this

[@rajpulapakura001]

amazing tutorial

[@cipher4811]

well explained thanks

[@GregHogg]

You're very welcome!

[@ahmedaj2000]

thanks!

[@beauclark2199]

Great vid, but slow down your speaking so lesser experienced people can follow along!

[@GregHogg]

Thank you. If it is too fast, there is a slowdown option :)

[@beauclark2199]

@@GregHogg Yes, i can absolutely slow down the speed of the tutorial. Have a great day

[@GregHogg]

@@beauclark2199 great! You too!!

[@spider279]

Too long and boring this guy is not straighforward

[@GregHogg]

Ikr Screw this guy

[@jawadidrees-hs9gc]

The techniques you showed were very important but don't know why you were ruining it by training on illogical models. I understand that it was just for test purpose but you could do better by training some logical models.

[@ShaneZarechian]

at 4:30 after looking it up, "frac=1" sets the fraction of the dataset to shuffle to be 100% of the total dataset, instead of say 25%. didn't really explain that properly

[@crepantherx]

Buddy, I have subscribed you. Please keep uploading more video that helps a lot

[@GregHogg]

Thanks so much, and I absolutely will!

[@mikekertser5384]

I've waited for this video! Many Thanks! :)

[@GregHogg]

Yup, I know you have been. No problem Mike and thank YOU 😄

[@ashleyb5849]

Amazing video as always! Super helpful

[@GregHogg]

Glad you thought so!

[@alanjohnstone8766]

Well explained., but why do you keep turning everything into numpy arrays? I do not think it is necessary.

[@GregHogg]

I guess it's just a habit... Probably not necessary yeah

[@hosseiniphysics8346]

tnx

[@Asoville18]

Thanks man

[@nokroman]

i've been lost in feature engineering chapter on the book that i am currently ready for machine learning right now, and straight ahead i found your video, and with the whole 47 minutes i have learned 2-3 things from you and i understand the whole process lot more better now, this all thanks to you Greg! keep up making these types of videos bcs WE NEED YOU!!

[@GregHogg]

Awe this is so nice. So glad to have brought a bit of value. Thank you so much for the encouragement and support, it means a lot. Happy learning 😄

[@zsnowboardern9332]

great video. pkease talk slowe for the beginners

[@GregHogg]

Thank you!

[@nhinvalerabayugao1418]

many thanks

[@GregHogg]

Many welcomes!

[@hsoley]

Best of the best! Thank you greg, you are the man!

[@GregHogg]

Thanks so much! I really appreciate that.

[@ShadowD2C]

really great comprehensive video, would be great if you did one on how to select features to get the best results for this problem itself

[@findoc9282]

this kind of way is what we need outside the uni class. Enough for PCA knowledge in Uni, let's code!

[@darshanprabhu3674]

Thanks a lot greg , you have helped me a lot through this videos.

[@lazytocook]

do u have a tutorial on catBoostencoder

[@ernestoflores3873]

Thanks! Very usefull!

[@agoodfriend365]

thank you bro

[@trongnhantran3358]

Love it

[@seemunchan1664]

Superb

[@jacobsquires]

You explain everything in such an easy-to-follow way! Thanks for the amazing video!

[@GregHogg]

Ghost to hear it! Thank you so much, and you're very welcome!

[@prasadchavan3562]

great🙌🏻, very helpful keep making more such videos

[@GregHogg]

Thanks so much, will do!

[@adilbhatti6299]

Wish I could give more likes!

[@GregHogg]

One will do ☺️

[@runbama1013]

great job thank you

[@GregHogg]

You're very welcome!

[@mouadet-tali4089]

thank you so much

[@GregHogg]

You are very welcome!!