Kaggle's 30 Days Of ML (Competition Part-2): Feature Engineering (Categorical & Numerical Variables)

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This video is a walkthrough of Kaggle's #30DaysOfML. In this video, I will discuss over 10 different feature engineering techniques that you can apply for categorical and numerical features. #FeatureEngineering

Note: this video is not sponsored by #Kaggle!

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like, subscribe and share to help me keep motivated to make more amazing videos like this one ;)

abhishekkrthakur

Thank you for doing this. Your videos guide me and help learn many new things. I was self studying way before the 30 days challenge with kaggle learn but don't know where to go or where to start even after finishing the kaggle micro courses. I really appreciate all the great work you're doing for this community. Thank you, Sir.

linnhtet

Thank you, Abhishek. This helps a lot. Highly appreciate the time and effort that you put into the creation of these videos.

yogitad

Thank you so much, this is really helpful. There aren't many Machine Learning practical tutorials like yours. I regret that you did not start to record videos like this one sooner.

longqua

Great job Abhishek sir. Really fruitful.

kamalchapagain

I needed this, i sooo very badly needed this . thank you so very much Abhishek ❤️

malawad

I'm learning so much from these videos, thank you so much

abirhasanx

This is like learning from the master of the craft.

orjihvy

Going through your book while going through these videos at the same time is like next level learning

lucaspimentel

Your videos are very helpful and make learning new topics and concepts so much easier! Thank you!

seemasharma-mnfk

I feel the numerical features are already standardized.

sujitmohapatra

First of all Thank you so so much for all your videos related to the course topics and now these ones for providing additional understanding for the competition. I have one question - you said that along with feature engineering we need to do hyper-parameter tuning, typically, do we need to tune model differently when we use the different techniques or we can apply same for all methods?

ninaddate

Learning from the best, as it should be done! I have a small query, are we free to create new features however we want to ? As long as our logic holds and it makes sense to the model, can we create new features independently without any restrictions or should we just follow some basic rules while creating one without experimenting too much on how to create one?

theonlypicklericktheonlypi

Before we concat the categorical cols back to the dataset after OHE, don't we need to drop those categorical cols from the DF first? Or does that not really affect the model predictions?

Orchishman

You know how you get so attuned to DS that you can listen to these like podcasts and not even have to look at the notebook to know what's going on.

snitox

Thank you it was really informative video. Do you think it’s okay to generate features by using frequency encoding of categorical features ?

shashihnt

Hi, great video Learning a lot from this.
one thing, interaction_only = True removes a**2 and b**2 so we are left with :- 1, a, b, ab
When we concat this with original dataframe doesn't it creates duplicates of a and b as a and b were already there.

codeu

Thanks a lot Sir, It was really helpful. 👍

GAURAVSINGH-nucu

Hi Abhishek,
I think there is a logical mistake when you use the generated features coming from groupby methods. You should have used the same groupby value obtained from the training set for training, validation, and test sets because the numbers of A and B are different for training and test sets.

aykutcayir

Thankyou so much sir for this helpful containt. Sir can you please make video of Data visualisation day of Kaggle Competition I am confuse in final project part of Data visualization please sir help me

AtulSharma-gftt

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