Polynomial Regression | Machine Learning

We simplify the concept, making it easy to understand how polynomial terms can enhance your regression models. Learn to apply Polynomial Regression in Python and elevate your skills in capturing complex relationships in your data.
Code used: github.com/campusx-official/1...
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⌚Time Stamps⌚
00:00 - Intro
00:10 - What is Polynomial Regression
07:25 - Code Demo

[@sanjaymange6904]

great video bro , excellent , perfect , no words to express my gratitude. U covered all the doubts I had w.r.t to polynomial regression

[@raj-nq8ke]

Perfect explanation as always.

[@MohitSingh-jb9tb]

If anyone wants to learn ML then it is the best channel.. explanation is amazing for all the topics.

[@balrajprajesh6473]

Best of the best!

[@saptarshisanyal6738]

Your explanation is wonderful. I request you to kindly prepare a video to explain the code.

[@alihasanmerchant9335]

Best, simple & easy explanation

[@colabwork1910]

Awesome Explanation.

[@amitbaderia6385]

Excellent. Very well explained. You should use real world data instead of random numerical values

[@rb4754]

You way of teaching is very very interesting.

[@krithwal1997]

Thanks for the Video Sirjiiii

[@JACKSPARROW-ch7jl]

Thank u nitish 🎉

[@heetbhatt4511]

Thank you sir

[@devendrasharma5567]

Infinit time thankyou ir

[@zainfaisal3153]

Sir you generated data in form X and y, what about, if we have real life dataset, then how to plot data distribution?

[@messi0510]

1:35-->7:25 What is polynomial Linear Regression?

[@0Fallen0]

6:03, PolynomialFeatures class for x1 and x2 and for degree 3, also adds columns (x1)(x2), (x1^2)(x2), (x1)(x2^2). Generally, for PolynomialFeatures set to degree = d, and we originally had n features, we will get a total of (n+d)! / d! n! features. Just thought this'd be useful to know.

[@ruchiagrawal6432]

thanks! I had the same doubt because at 5:52 sir didn't add the additional term x1*x2, so I got confused. Please correct me if I'm wrong.

[@thatsfantastic313]

​@@ruchiagrawal6432 it gets features’ high-order and interaction terms. suppose (x1,x1) becomes (1,x1,x2,x1^2,x1x2,x2^2) and so on

[@subhajitdey4483]

from where/how the term (X1)(X2) coming from ? Kindly elaborate

[@manishsinghmehra7555]

​@@subhajitdey4483 formula bro (a+b)^n where n is the degree of polynomial.

[@sandipansarkar9211]

finished watching

[@sanjeevranjanmishra8811]

Can we apply polynomial regression on dataframe (let's say df) first then after we split x into train_x or test_x. If yes then why everyone doing split first then transform x_train then x_test.

[@Sreedeviha]

I understood the explanation well.. thanks... but Bhai one doubt in real data set by looking at the data without plotting graph, will I be able to tell we need to use poly reg.. Also if we add features like X^2,X^3, will it not end up with multicollinearity as dependency b/w input features exists now??

[@thatsfantastic313]

Yes it adds multi-collinearity in your model by introducing x^2, x^3 features into your model. To overcome that, you can use orthogonal polynomial regression which introduces polynomials that are orthogonal to each other.

[@namanmodi7536]

nice

[@krishnakanthmacherla4431]

Wow

[@ahmadansari7708]

❤️

[@Ishant875]

Why in polynomial regression with degree 2 is not able to capture the bowl in the training dataset? I think that curve shown was for a higher polynomial.

[@Tusharchitrakar]

Yes even I was confused because the poly features should have fit better for degree 2

[@subhajitdey4483]

One qurstion.... 10:37 why we are not using fit_transform() for X_test_trans as X_train_trans ??

[@manishsinghmehra7555]

fit() is used to calculate mean and variance of the data, while transform() is use to transform (scale the data according to our need) the data. When we first use fit_transform() which already calculated mean and variance from 80% of data, which is more reliable than the 20% of data i.e. X_test_trans also by doing so we keep the mean and variance of whole dataset same that's why we only use transform for X_train_trans.

[@1981Praveer]

we might have to use "print("Input", poly.n_features_in_)" instead of "print("Input", poly.n_input_features_)"

[@hari_thaniwal]

Watch this video with the subtitles provided by KZbin. You will find another story in the subtitles 😅

[@rafibasha4145]

Hi Nitish bro,this audio is not clear ..please do new video if possible on same topic..also please cover fearure selection xgboost

[@balrajprajesh6473]

you can use earphones, it will be understandable.

[@anuragkothari8384]

You should have write the code from scratch for polynomial feature generation

[@manishsinghmehra7555]

Why do we create X_new and y_new, while we have X_test_trans and y_pred

[@randomlyaj4735]

Same doubt

[@khushisaini2683]

Avaj ni saaf

[@user-do1gs6sw3m]

❤