Tutorial 2- Feature Selection-How To Drop Features Using Pearson Correlation

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In this video I am going to start a new playlist on Feature Selection and in this video we will be discussing about how we can drop features using Pearson Correlation
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Sir, could you please upload more videos on feature selection to this playlist?
It is very amazing. I followed all the videos from feature engineering playlist. You are doing a great work.
Thank you.🙏🏻

ashishkulkarni

Being in a teaching profession, I assure this is the best explanation about Pearson correlation.. Please make more likes.

rhevathivijay

Sir your channel is a perfect combination of sentdex and statquest. You are doing a great work 🙌more power to you!!

prakash

I think instead of dropping "either of" 2 highly correlated features, we should check from both of them how each of them correlates with the target as well and then drop the less correlated with the target variable. Which might increase some accuracy instead of considering dropping whichever comes first. Again, I think it is.

waytolegacy

Very comprehensive explanation for someone from non AI background. Thanks Sir keep up the good work!

nurnasuhamohddaud

Sir, the videos you uploaded on feature selection helped a lot !, Please upload the rest tutorials and methods too! Eagerly waiting for it !

sukanyabag

Any word is not sufficient to thank you for your work sir ....🙏🙏

shubhambhardwaj

thank you sOOo much, perfect explaining :) good luck with your channel that is recomended

suhailabessa

This was incredibly helpful; thank you for the great content!

ActionBackers

If you are transporting ice-cream in a vehicle, the number of ice-cream sticks that reach the destination is inversely proportional to temperature, higher the temperature, lesser are the sticks.

If you want to effectively model the temperature of the vehicle's cooler and make it optimal, you need to consider this negatively correlated features, outside air temperature and number of ice-cream sticks at the destination.

rukmanisaptharishi

Well explained. Really great work sir. Thank you very much

RandevMars

Your knowledge is really invaluable. Thanks

neelammishra

GREAT CONTRIBUTION SIR.... THIS CHENNAL SHOULD 20M SUBSCRIBER🤘🤘

yashkhant

In which order should u do the feature selection steps?

0. Clean the dataset, get rid of NaN and junk values. Check format for datatypes in testset etc

1. Use z-method to eliminate outliers

2. Normalize the train_X data

3. Check correlation between x_train variables and y_train. Drop variables that have a low correlation with the target variable.

4. Use pearsons correlation test to drop highly correlated variables from x_test

5. Use variance threshold method to drop x_train variables with low variance.

All variables that have been removed from the x_train data should be removed from the x_test aswell.

6. Fit x_train and y_ train to a classification model

7. Predict y(x_test)

8. Compare the predicted y(x_test) output with y_test to calculate accuracy

9. Try different classification models and see which one performs the best (have the highest accuracy)

Is this the right order? Have I missed something?

andyn

amazing teaching skills you have bhaai ... THNX

suhailsnmsnm

Thanks so much! very useful. you are so good

nahidzeinali

Nice! please upload more on this topic!! thank you!

tigjuli

I think the abs is important since it's like having two rows one being the opposite of the other

alphoncemutabuzi

I think it all depends on domain that whether to involve the neg corr or not, or we can train two diff models and compare their scores, Thanks Sir

gurdeepsinghbhatia

great video. very informative and educative. Thank you

gabrielegbenya

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