Outlier detection and removal using IQR | Feature engineering tutorial python # 4

IQR is another technique that one can use to detect and remove outliers. The formula for IQR is very simple. IQR = Q3-Q1. Where Q3 is 75th percentile and Q1 is 25th percentile. Once you have IQR you can find upper and lower limit by removing this formula,
lower_limit = Q1-1.5*IQR
upper_limit = Q3 +1.5*IQR
Anything less than a lower limit or above the upper limit is considered outlier. We will use python pandas to remove outliers on a sample dataset and in the end, as usual, I have an interesting exercise for you to practice

Topics

00:00 What is percentile and IQR
04:15 Remove outliers using IQR
06:55 Exercise

[hr]

Came back to this video after a long time. Just noticed that the video description itself explains everything straight to the point Sir. Great job

[maseedilyas]

Thanks man very useful you are the best . I've watched your entire machine learning tutorial . I learned many things from this.

[shreyasb.s]

The way of explaining in simply way is very good. Thanks a lot .

[leamon]

Thanks a lot. Awesome tutorials. Looking forward to more amazing contents in Feature engineering series.

[e-normous]

Thank you so much for this very helpful video. You managed to explain the needed concept straight to the point. Awesome!

[sumitrawat]

You always teaches complex topics with so much ease, great lec...sir please add more tutorial in list

[rajivmehta]

Thank you, such a simple explanation about the IQR and Python Code too!

[DirrrtyD]

Thank you! I needed to address outliers in a data set that I was performing an ANOVA test on, and this helped a lot.

[anushkajain]

Kudos to sharing your knowledge in such a simplified manner. Exercises at the end gives a huge confidence. Thanks !! :)

[humayunnasir]

Amazing Video Series. You are the best teacher on internet. Huge Respect from Pakistan.

[jonathancampos]

I am pretty much thankful for this video sir, I improved my understanding!

[kashifiqbal]

Such an amazing video series, Thank you very much sir, keep continue.

[iaconst.]

eres un excelente profesor, entiendo la explicacion en Ingles perfectamente, desde peru, gracias por compartir tus conocimientos!!

[vidhyasagar]

Thanks for another amazing video.Could you also please make a video explaining different scenarios on how to decide when to use which Outlier technique?

[kirandeepmarala]

such an amazing series..Thank you very Much Sir..I am Learning A lot daily from You Sir..

[salvinjohn]

Most underrated YouTube channel ...even though he teaches much better and easy to understand than so many YouTube channels which confuse you with lot of stuff.

[maheshpeddykudi]

Interested VS committed.... awesome.... one have to be it's very clear now...thanks for the resources bro...

[hardikatri]

Just Awesome. Really loved the teaching style. Waiting for more tutorials..

[mouaztabboush]

All I can think about is aamir skipping the stairs and entering through the windows xD
btw. great tutorial!!

[ashwinsg]

Thank you so much Sir for this amazing techniques. Though one request, could you please make some tutorials on how to select best features from the dataset i.e. FEATURE SELECTION