How to do Multiple Linear Regression in Python| Jupyter Notebook|Sklearn

[@cowjacketstudiostm5902]

Literal queen. Been crying for a week over this. I could've just watched this, this is amazing.

[@omarelliottgreen823]

After 6 hours of stumbling through StackOverflow and various books, this video made it clear in 20 minutes! Thank you SO MUCH!!!

[@b048peyyettipavankartikpra4]

Amazing video! This is my first project ever and I hope to continue further in my career of Data Science!

[@mizanalisayed]

Since 2 days i am trying to understand ML. Finally abhi ache se samajh gaya. Thanks

[@shahzebmohammad9521]

Simple, Clear, Concise. What else do you want?

[@MeghaNarang]

Thank you!

[@JewelWildmoon]

Quick extra note for anyone since I was stuck on it for a bit. If you have any columns you want to exclude beforehand (for me, it kept picking up data with string values too and I wanted to exclude those), run this first for those columns before defining x and y: data_df.drop(['Column1', 'Column2', 'etc..'], axis=1, inplace=True) Using "inplace=True" will make it so those columns will stay out of the dataframe because if you define it as "inplace=False" or don't define it at all, those columns you removed will go back into the dataframe anyway. It wasn't used in defining x and y because we need the column PE to return to the dataframe. And thank you so much for this video miss Megha. I'm new to Python and have been struggling with this for an assignment for hours and this really helped me.

[@vikrantyadav7379]

This is the best tutorial i have come across ... simple ,easy and beautiful. Please upload other regression and classification problems.

[@tammu478]

Beginning my ML journey. Thank you for the crisp explanation.

[@JawadAhmadKhan]

one of the best explanations in very simple words... Bravo Miss Megha Narang

[@nihinlolakukoyi5987]

In wish I can give this video a million likes.... thank you very much.. this video was really helpful.

[@SKini18]

Very nice. Exactly what I was looking for

[@special1_28]

Hi Megha, you are a mega tutor indeed. You're more than awesome. Without any gaining, this is the best explanation and best perspective I have come across on youtube regarding ML. You're superb ma'am. I await more of your uploads. Thank you!

[@sudarshandev6369]

Wow really its very easy to understand, Mam your sequence wise explanation is awesome.

[@CFDAsmaaHadane]

Thank you for this very informative tutorial! Please keep uploading

[@rabiulmallick8991]

thank you miss

[@wayneemery9276]

Should always perform a check for assumptions of Linear Regression when performing it. Otherwise, it can be dangerous or misleading.

[@sixlife.official]

Do you have any good material for guiding how to check those assumptions using the exercise presented in the video?

[@indointanchannel]

Thanks sist, you help me to understand about a long code becomes a short code. It's a smart video.

[@mallelasunny]

Hi Megha...the explanation is neat and clean...right at the point. Very beautifully explained and the concept is clear. Can you please upload more videos on Logistic Regression, KNN, Random forest, Support Vector machines, Decision tree etc?

[@taniacai5245]

Hi Megha, thank you so much for the video! It helped me a lot in work. Really appreciate! hope you keep making that

[@MeghaNarang]

Thank you

[@avinashsiripuram1058]

Great explantion ............ur work must be admired

[@samarthchakrawartiblogsand4809]

For a beginner this video is a big help!!

[@Pimentocompany_]

Hello I have this problem how can I solve it? (could not convert string to float: 'rainy')

[@yangelena7135]

Hi thank you for sharing. But I am wondering how do you get the actual linear regression equation with sklearn

[@hanifullah1088]

Very clear and to the point . . . .kindly make similar videos for each topic such as Decision tree classifier etc

[@resreng]

Thank you so so so much for clearing the concepts.

[@ranulmbalasooriya659]

Thank you so much for the clear explanation.

[@syeda8343]

Mam I got an error ln[8] Could not convert string to float:AT How to fix it? Can anyone tell me?

[@bonganindlovundlonu3239]

Awesome video. keep on doing great

[@lookaway8496]

So good! this is much simpler explanation. I love it

[@cass1522]

Thank you so much for the video!

[@arjunramprasad6752]

Amazing tutorial!

[@Deepsim]

I really like your well organized presentation structure!

[@NK-vd8xi]

What keyboard are you using? It sounds so soothing.

[@EddyFisico]

This is a very good video! Thank you very much!

[@emanuelbyas9819]

Thank you so much! Super Clear!

[@MeghaNarang]

Glad it helped!

[@jiwan-darshan.]

If we have two outputs in multiple linear regression model. What are the steps ?

[@anuarohi2335]

Very meaningful session, great explanation 👍

[@abdullahateeq1236]

Thank you for the visualization.... :)

[@ShiftKoncepts]

Do you have a guide on how to do multiple variables if they are non linear? Meaning we’d have to use a polynomial method with degrees?

[@KiratAlreja]

Mam, how do we predict multiple target values (y variables) with a single linear regression model?

[@vinayshukla3800]

GREAT WORK MEM👏👏👏👏👏

[@datawithalvin]

thank you so much this video help me to understand the concept faster

[@MeghaNarang]

Glad to hear that!

[@jaydeepraut820]

Hi, your videos are awesome and easy to understand. Can you please upload the logistic regression, random forest, SVM and times series modeling videos with examples.

[@MeghaNarang]

sure, will try to upload something soon

[@jaydeepraut820]

Thank you very much, and please upload with the dataset available on the internet, so that we can try on our own.

[@billwindsor4224]

@@jaydeepraut820 did you try to search in Google for the dataset described, as she showed and explained it at the video timestamp @1:10 and following? As she describes this in the video, it is straightforward to search and find from Google.

[@Kod.u]

Really helpful tutorial! Thank you.

[@shantanulokhande1792]

Thank you so much. It was really helpful!!!

[@yusiferzendric1489]

I only had difficulty in plotting the model, thanks a lot 😃😃👍👍

[@APPRENTINazmi]

very nice and easy teaching. Congrats.

[@MeghaNarang]

Thanks!

[@samahabdalrahim3709]

thanks a lot, please I want to know which algorithm is used (batch, stochastic...?) also can we show the cost function?

[@wahu9578]

@ Megha, thank you for this. Have you done another video of a way to improve the model? If so, can you kindly share the link?

[@olanrewajubakare3790]

I really appreciate your work, however, I will like to point something I noticed out. The scatter plot you created for your result at the end of the analysis where you had the y_test plotted against the y_pred seems inaccurate to me. plt.scatter(y_test, y_pred) is supposed to indicate that your y_test is on the x-axis while your y_pred. is to be plotted on the y-axis. I believe what you should do is showcase the y_test and y_pred on the y-axis while you use a common x-axis for the two on the same plot.

[@christopherwright361]

Two different ways of showing the same basic thing. y_pred vs. y_test will form a 45-degree straight line if the data is perfectly predicted, whereas y_test and y_pred both vs. x_test will show the points overlapping each other, if perfectly predicted. However, because this is multiple regression, there isn't a single x variable, so you would have several of the latter plots. These are useful for certain diagnostics. However, a very common first plot is y_test vs. y_pred. Plotting residuals vs. y_test or x_test (one at a time) are also common charts to make.

[@ashiskar9531]

Hi megha why did not you scaled the dataset.

[@waqasrana7891]

Hi Megha, thanks for tutorial. what if we have string in datasets (like types can be multiple strings not boolean e.g colors:blue, red, green ,black ) how we will convert it into float format cuz model only understands numbers.

[@symonhalder391]

Error showing after fit model. Showing could not convert string to float.

[@DVijebahu]

Clear and Nice explanation....

[@MeghaNarang]

Thank you

[@akshayjadhav2003]

After define x & y print statement should be came in " str " how it is possible if all dataset and format as it is copy .🤔

[@sadhanabarman3178]

Ma'am can we get regression for more than 2 independent variables w.r.t more than two dependent variable??

[@owonubijobsunday4764]

I liked the video, so I "Liked" and I "Subscribed" Thanks, MN

[@ca2hill]

So useful!!! Thank You

[@greenfootprint2680]

Great breakdown! Liked and Sub 👌

[@toluwasedaniel7064]

Hello!! I keep getting this popped up message anytime i tried to import the data. Please help! "The cell has not been executed to avoid kernel deadlock as there is another pending input! Submit your pending input and try again"

[@mansoorahmed4150]

could you please tell more, using multiple regression which technique you follow? I mean OLS or else?

[@prachijain3736]

could not convert string to float: 'Next Look' Getting this error, How can I resolve it.

[@tahirakhurshidali1506]

Hi i am findinf error in importing excel file, "SyntaxError: (unicode error) 'unicodeescape' codec can't decode bytes in position 0-1: truncated \UXXXXXXXX escape" Can you please help me to know how to resolve it?

[@saeedewu129]

how to find the beta coefficients and the constant value

[@prabirdas8430]

Nice explanation 👍🏻

[@MeghaNarang]

Thank you!

[@myandrobox3427]

Excellent💯👍

[@060584saurav]

How can we see the equation of the model

[@pinkysaikiadutta9752]

Very nice explanation. Thanks!! for the explanation

[@humphreymorara7818]

At 13:20 under predict Is it supposed to be y_pred =...... x_test or y_pred =...... y_test since you are predicting y not x

[@dianahnyamweya1393]

What happens if one of the variables has string values instead of numerical values?

[@meditation_central299]

Hi ,What kind of MLR is this ?

[@lentskababe]

hi what can i put when i need to drop 2 variables

[@chandrashakhar1800]

Could you please make a video on this topic from scratch without using the sklearn library to better understand the mathematics behind it or provide a useful link? Please...

[@billwindsor4224]

@Chandra Shakhar start with the website she shows in her discussion of sklearn: scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html , which leads you to the technical explanation at: scikit-learn.org/stable/modules/cross_validation.html#cross-validation

[@norahassan3407]

very insightful many thanks for your impressive work

[@roshukhanal4737]

How to do the path for first part? where to storedata

[@minhngo3162]

Hi Megha, thanks for showing steps by steps. I have a question. Instead plotting the result by "Actual" and "Predicted", can we visualize the predicted vs. actual ''y" for each variable"x"? Can you please advise the codes? Thank you.

[@ayoajayi280]

Hello Megha. Great video. But you did apply the scaler function to standardize the days. Why?

[@ikennanwankpa6354]

Thanks for the video. I built a model to predict rent across my country. The accuracy score is 43% or so. What can I do to improve it? I can send the script of needed. Thanks.

[@srikanthgiri2781]

hi , Thanks for this amazing video! If we need to print the linear regression equation in the form of y= a+bx1+cx2 , how to do that?

[@googm]

Do `dir(ml)` and you will see a list of attributes. You'll find what you're looking for in ml.coefs_ and ml.intercept_.

[@renato.aravena4051]

Cleannn tutorial, the best of all, thx :))

[@girishchhonkar9391]

Great explanation mam...just simple and smooth 😃..keep uploading videos

[@alyaaazlan7794]

This is sooooo Great!!!

[@pursuingperfectionn]

Simply Brilliant !!

[@akeisaac7278]

I keep getting a value error , could not convert string to float :’AT’

[@vvarsani2618]

Error: name 'y _test'is not defined What should i do??

[@sreekanthreddy7452]

Keep it up and make videos on other models too mam.

[@motuagrakalam7749]

Really nice explanation. One question, in the final regression equation can we have 0 as coefficient for any independent variable or all variables will be assigned some non 0 values?

[@souptikdasgupta2062]

It should always have a non zero value because if it is zero it would mean that the specific independent variable is totally useless to predict the dependent variable, which never is the case.

[@christopherwright361]

Because you're estimating from a sample, it would be nearly impossible for a variable to have a coefficient of 0, even if there isn't any correlation between the dependent variable and the independent variable (within the context of your model) in the real process. If you ever get a zero coefficient, it's likely due to an error such as including a redundant feature, having perfect (multi)collinearity, including k dummy variables (and an intercept) for k cases, etc. This can be fixed by reducing the feature set (one at a time, of course.)

[@azizinaim8470]

hi, can i ask, why my df not defined , do i need to do anything before put the coding as you..im a beginner, would be great if you can reply this :)

[@shakirullah5840]

Very nice video. Thank you so much and Best of luck--Shakir, Bangladesh

[@dhaneshsarpale1718]

simple explanation... Thank you mam

[@MeghaNarang]

Thanks @Dhanesh

[@jongcheulkim7284]

Thank you so much. This is very helpful.

[@gaurizade7560]

Why you did not check if multicolinearity present or not ?

[@sunandadebnath96]

you are life saver

[@danielstafford6858]

brilliant video

[@lekhrajmalviya4927]

Very nice explaination. Could you please tell how we can get equation for the predicted model?

[@shashibhushan715]

how to perform regression for the following equation: time= ( pressure)^a + (concentration of fuel)^b + (concentration of oxidizer)^c + exp(d/Temperature). Here, time is dependent variable on pressure, concentration of fuel, concentration of oxidizer and temperature. How to set non-linear model like the above equation

[@atoffice7323]

very nice presentation..

[@MuhammadAswadking]

Dear Megha, I am from Pakistan,I have followed your code,but I am getting this error while plotting. ""x and y must be the same size""" would you please explain Thanks in advance.

[@SanaShaikh-sm7kz]

Thanks a lot..this video has helped me a lot in my project❤❤❤❤

[@MeghaNarang]

Glad I could help :)

[@tarunkumargola7633]

Finding the parameter at which cost function gives minimum value(gradient descent) is done by scikitlearn?

[@MeghaNarang]

towardsdatascience.com/minimizing-the-cost-function-gradient-descent-a5dd6b5350e1