Multiple Linear Regression | Part 3 | Code From Scratch

Dive into the mathematical foundation of Multiple Linear Regression in this third part of our series. We'll guide you through the formulation from scratch, making it easy to grasp the concepts behind this powerful regression technique. Build a solid foundation for your regression modeling skills.
Code used: github.com/campusx-official/1...
Other reading material:
scikit-learn.org/stable/datas...
www.tutorialspoint.com/numpy/...
www.tutorialspoint.com/numpy/...
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[@ComputerScientist2027]

Thankyou Sir.

[@ytg6663]

बेहतरीन बहुत बहुत धन्यवाद ❤️🔥🔥

[@kidscreator2268]

you are doing great job sir

[@ParthivShah]

Thank You Sir.

[@uditjec8587]

I will spread your videos wherever I can. Underrated channel. Very sad

[@subhajitdey4483]

Thank you for this video, Nitish Sir. Sir I am stucked in one place. After creating own class (MyRegression) when calling the fit method, it's showing a long error, showing dtype('o') can't converted to dtype('float64'). Then I've resolved it by converting the X_train and Y_train object type to float64 type before calling the fit method. Then error is solved but the X_test output , coefficients, intercept are coming different compare to the inbuild class. please help Sir.🙏🙏

[@AxayPatanvadiya]

why convert to inverse matrix. this scenario i am not understood pls can someone help me

[@seeker7793]

why we are using n+1 ??

[@harshbhagwani523]

sir i would like to ask 1 question when we are predicting in MeraLR we are doing dot product of X_test and self.coef_ +se;f.intercept so at that time self.coef shape is (9,1) and X_test shape is (89,10) so how we will do dot product ? because we are excluding the intercept_ from beta

[@univer_se1306]

self.coef is (10,1) that means total(intercept+coff)=11

[@raghavagrawal6263]

Sir please take the series forward.

[@campusx-official]

Will be uploading the next video tomorrow.

[@raghavagrawal6263]

@@campusx-official yes sir please.

[@abhishekrajravi6155]

@@campusx-official Yes sir please complete this series

[@todaystrending1992]

Yes sir please 🥺

[@stevegabrial1106]

waiting for new video, see u soon.

[@campusx-official]

Tomorrow

[@soujanyakalita4754]

Sir, Simple Linear Reg mein ye code kaam nhi kr raha hai. I have tried everything, Can you please clarify it. Thank you.

[@readbhagwatgeeta3810]

Actually the formula of beta is not correct...there is a correction in that value

[@soujanyakalita4754]

@@readbhagwatgeeta3810 thank you sir, ya sir beta ka formula meine change kiya tha since thoda wrong tha but still not working, aap please beta ka end formula yaha likh doge kya. So, that i can cross check with mine. Thankyou.

[@ronylpatil]

have you understood how we got output of matrix differentiation?

[@vamshikumar2676]

It works actually i tried it. It just that at np.insert(X_train, 1,0,axis=1) try this np.insert(X_train.values, 1,0,axis=1). Because, in this case sir directly took numpy array for X_train, but generally when we split we get X_train as a data frame, so we need to convert it into numpy array to insert another row at the first column.

[@shivoham5939]

reshape your x_test and y_test data by X_test.reshape(col_size,1) and y_test.reshape(col_size,1)

[@krishnakanthmacherla4431]

You are impossible dude in explaining

[@campusx-official]

As in? bad or good?

[@krishnakanthmacherla4431]

@@campusx-official good obviously, i have never been this consistent at learning

[@campusx-official]

@@krishnakanthmacherla4431 thanks boss. Keep learning

[@univer_se1306]

the only change i did in meraLR class is that i calculated betas in diffferent fashion-- betas=np.dot((np.linalg.inv(np.dot(X.T,X))),(np.dot(X.T,y))) ,by me and by sir betas = np.linalg.inv(np.dot(X_train.T,X_train)).dot(X_train.T).dot(y_train) although same must give same output but i am getting different r2Score plzz clarify the doubt

[@saloneesays]

it works 🥲

[@kidscreator2268]

plz make statistic videos

[@campusx-official]

Statistics for Machine Learning: kzbin.info/aero/PLKnIA16_RmvbVrE0eZO2bCaFln6jaNq-1