Feature Engineering Techniques For Machine Learning in Python 

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

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.

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!!

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

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

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

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

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

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

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

Amazing video as always! Super helpful

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

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

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

Thank you so much. Your efforts are really appreciated.

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

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

Thanks! Very usefull!

This is very informative....

great🙌🏻, very helpful keep making more such videos

great job thank you

Thanks man

many thanks

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

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

tnx

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

amazing tutorial

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

well explained thanks

Love it

Superb

great video. pkease talk slowe for the beginners

thank you bro

thanks!

thank you so much

do u have a tutorial on catBoostencoder

Wish I could give more likes!

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.

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

Too long and boring this guy is not straighforward