Linear Regression Algorithm In Python From Scratch [Machine Learning Tutorial] 

I recommend this video for those who understand the general concept of linear regression, but want to know what happens 'under the hood'

Amazing tutorial. Difficult concepts were explained with such ease. Kudos team Dataquest!

This is absolutely amazing and great video. I can’t wait to see more great work

Very explicit. You are a wonderful teacher. Thanks so much

this is a great tutorial. Beautifully explained.

Very well explained!!!

Thank you . It was well explained.

Thanks so much. Better than any E-books 🙂

Great and very clear explanation. The only point missed in the end is the regression visualisation 😉. Nice to have both initial data and the regression plotted

Thanks so much for the Video

Perfect,,, thnks a lot

Today you will my teacher. I'm from VietNam. Thank you so much

great job

THANKS ALOT🤯

thanks for the lesson, but just a question, during the model the separation of x,y_train and x,y_test was not made, why would it not be necessary, and if it is necessary to do it, how would it be done? thanks

Would the solution for B be considered a least squares solution? Also, If we wanted to construct say a 95% confidence interval for each coefficient, would we take B for intercept, athletes, and prev_medals (-1.96, 0.07, 0.73) and multiply them by their respective standard errors and t-scores? Would the formula would be as follows: B(k) * t(n-k-1, alpha = 0.05/2) * SE(B(k)) , or does this require more linear algebra? Great tutorial btw, thanks for the help.

Hi Vikas, which is better for GLM models in python: sklearn or statmodels package?

Do you have an example like this with multiple x-values or features?

Is there a reason you chose to implement the normal equation over gradient descent? I'm quite curious as I am more familiar with gradient descent.

One correction, not relevant to the actuall regression, but should be said nonetheless. The number of medals one athlete can win is not limitted to one, rather it is limited to the number of events the athlete competes in (maximum of one per event). In fact, numerous athletes have one multiple medals in one Olympics. Just wanted to clarify that. Of course, from a certain number of athelets, it will be impossible for a smaller team to compete in as many events as the large team, making it more likely that the larger team wins more medals.

Hey, That is a great beatiful demonstration of linear regression. Thank you. But I didn't understand where prev_medals coming in building X matrix at the beginning? some one can give to me explanation on apparution of these value inside the X matrix?

Hi, this is a wonderful explanation. Great job putting this together. The only thing that really confuses me is how you factor in previous medals in the predictive model. What would that look like in the linear equation at 1:54?

Why do we need to add those "1" when solving the matrix

tnx sir

Can you please make a video demonstrating the multivariate regression analysis with the following information taken into consideration? Performs multiple linear regression trend analysis of an arbitrary time series. OPTIONAL: error analysis for regression coefficients (uses standard multivariate noise model). Form of general regression trend model used in this procedure (t = time index = 0,1,2,3,...,N-1): T(t)=ALPHA(t) + BETA(t)*t + GAMMA(t)*QBO(t) + DELTA(t)*SOLAR(t) + EPS1(t)*EXTRA1(t) + EPS2(t)*EXTRA2(t) + RESIDUAL_FIT(t), where ALPHA represents the 12-month seasonal fit, BETA is the 12-month seasonal trend coefficient, RESIDUAL_FIT(t) represents the error time series, and GAMMA, DELTA, EPS1, and EPS2 are 12-month coefficients corresponding to the ozone driving quantities QBO (quasi-biennial oscillation), SOLAR (solar-UV proxy), and proxies EXTRA1 and EXTRA2 (for example, these latter two might be ENSO, vorticity, geopotential heights, or temperature), respectively. The general model above assumes simple linear relationships between T(t) and surrogates which is hopefully valid as a first approximation. Note that for total ozone trends based on chemical species such as involving Chlorine, the trend term BETA(t)*t could be replaced (ignored by setting m2=0 in the procedure call), with EPS1(t)*EXTRA1(t) where EXTRA1(t) is the chemical proxy time series. This procedure assumes the following form for the coefficients ALPHA, BETA, GAMMA,...) in effort to approximate realistic seasonal dependence of sensitivity between T(t) and surrogate. The expansion shown below is for ALPHA(t) - similar expansions for BETA(t), GAMMA(t), DELTA(t), EPS1(t), and EPS2(t): ALPHA(t) = A0

Hi guys! Very interesting indeed! There is one thing I don't understand though. The identity matrix, as you mentioned , behaves like one in matrix multiplication when you multiply it with a matrix of the same size. But in this precise case (around the 13:08) the matrix B doesn't have the same size. So how come you can eliminate the identity matrix here from the equation? Thanks!

It is such a fantastic explanation of Linear Regression. My question is, is there any possibility that we can't obtain the inverse of matrix X?

In predictions we got values as 0.24,-1.6,-1.39 so can you explain does -1.6 medals is valid? Or I need to use some other dataset to perform regression like house prediction? Can you suggest me some dataset in which i can apply ridge regression?

Thank you for this video. Could you please share the ppt slides of this lesson?

Hi Team... Very well explained Linear Regression from scratch... Do you have any video for Ridge Regression from Scratch using Python?

This guy is old, young, sleepy and awake all at the same time.

I am wondering is it okay to have a model which predicts country to receive negative amount of medals? Isn't that just impossible?

I rather use statsmodel than using this mthod which makes things complex

I guess another question I have is how to invert a matrix

sorry teacher. i guess you were confused SSR was SSE and R2 = 1 - (SSE/SST) = SSR/SST