How to do Multiple Linear Regression in Python| Jupyter Notebook|Sklearn
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- Опубліковано 21 лип 2024
- If you are new to #python and #machinelearning, in this video you will find some of the important concepts/steps that are followed while predicting the results in Multiple Linear Regression model. These steps are helpful, especially, while handling large number of observations in a dataset.
The dataset used in this video is publicly available and therefore, easy to download and practice.
Here is the link to the dataset: archive.ics.uci.edu/ml/datase...
(Note: If multiple sheets are downloaded, the data in the sheet 1 is used in this video for analysis and if your data is downloaded in excel format, replace pd.read_csv with pd.read_excel while importing dataset in Jupyter)
Hope you find this video helpful. Let me know your thoughts in the comment section below.
I make videos on Tableau and Python on a regular basis. If you like this video, please don’t forget to subscribe.
Happy Learning!
#multiplelinearregression #python #jupyter #jupyternotebook #beginnerinmachinelearning #machinelearning #pythontutorial #regression #scikitlearn #sklearn
Literal queen. Been crying for a week over this. I could've just watched this, this is amazing.
After 6 hours of stumbling through StackOverflow and various books, this video made it clear in 20 minutes!
Thank you SO MUCH!!!
This is the best tutorial i have come across ... simple ,easy and beautiful.
Please upload other regression and classification problems.
Since 2 days i am trying to understand ML. Finally abhi ache se samajh gaya. Thanks
Beginning my ML journey. Thank you for the crisp explanation.
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!
Amazing video! This is my first project ever and I hope to continue further in my career of Data Science!
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?
Thank you for this very informative tutorial! Please keep uploading
I really like your well organized presentation structure!
So good! this is much simpler explanation. I love it
Wow really its very easy to understand, Mam your sequence wise explanation is awesome.
Thank you so much. It was really helpful!!!
Thank you so much for the video!
Really helpful tutorial! Thank you.
Hi Megha, thank you so much for the video! It helped me a lot in work.
Really appreciate! hope you keep making that
Thank you
Very clear and to the point . . . .kindly make similar videos for each topic such as Decision tree classifier etc
For a beginner this video is a big help!!
I only had difficulty in plotting the model, thanks a lot 😃😃👍👍
This is a very good video! Thank you very much!
Thank you so much for the clear explanation.
Thank you so so so much for clearing the concepts.
one of the best explanations in very simple words... Bravo Miss Megha Narang
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.
Great explantion ............ur work must be admired
This is sooooo Great!!!
Simply Brilliant !!
Hi thank you for sharing. But I am wondering how do you get the actual linear regression equation with sklearn
Amazing tutorial!
Great explanation mam...just simple and smooth 😃..keep uploading videos
Thank you so much! Super Clear!
Glad it helped!
Cleannn tutorial, the best of all, thx :))
thanks a lot, please I want to know which algorithm is used (batch, stochastic...?) also can we show the cost function?
Simple, Clear, Concise. What else do you want?
Thank you!
Thank you so much. This is very helpful.
Very meaningful session, great explanation 👍
Awesome video. keep on doing great
@ 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?
Thanks sist, you help me to understand about a long code becomes a short code. It's a smart video.
Great breakdown! Liked and Sub 👌
So useful!!! Thank You
Excellent!
Very nice explanation. Thanks!! for the explanation
you are the best!!
very insightful many thanks for your impressive work
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.
Thank you for the visualization.... :)
Fantastic tutorial. Question I have is if I wanted to test my model on another dataset how could I do it once I have my coefficients and intercept? Best Wishes Peter
thank you so much this video help me to understand the concept faster
Glad to hear that!
you are life saver
In wish I can give this video a million likes.... thank you very much.. this video was really helpful.
could you please tell more, using multiple regression which technique you follow? I mean OLS or else?
Mam, how do we predict multiple target values (y variables) with a single linear regression model?
Very nice video. Thank you so much and Best of luck--Shakir, Bangladesh
Hi Megha, thanks for this great video, very simple question may be for you, on your predicted values chart, is there a way to plot a straight line across those values?
Like simple linear regression?
Thank you!
THANKYOU GREAT VIDEO
I liked the video, so I "Liked" and I "Subscribed"
Thanks, MN
Clear and Nice explanation....
Thank you
Hi Thanks for the informational video, it is very easy to understand.
GREAT WORK MEM👏👏👏👏👏
simply awesome
What keyboard are you using? It sounds so soothing.
brilliant video
thx for video a lot. But what about the prediction of finding most minimum value in according to independents variables. How can we find it? thanks in advance
Thank you so much ❤️
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.
simple explanation... Thank you mam
Thanks @Dhanesh
Excellent💯👍
Very nice explaination. Could you please tell how we can get equation for the predicted model?
very nice and easy teaching. Congrats.
Thanks!
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?
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_.
Thanks a lot..this video has helped me a lot in my project❤❤❤❤
Glad I could help :)
Gracias, me ayudo mucho tu video :)
Great Ma'am
Brilliantly explained. Can u make video for deployment of model to use with webpage/android or any programming language GUI through API . Also make such a beautifully explained video for ANN also.
thanks for this video
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?
Keep it up and make videos on other models too mam.
Very useful
Very Nice Megha..Just if you give some explanation of functions which are using then it will be more clear. Nice Attempt!!!
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?
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.
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.)
Nice video mam .. God bless you ☺️
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.
sure, will try to upload something soon
Thank you very much, and please upload with the dataset available on the internet, so that we can try on our own.
@@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.
Finding the parameter at which cost function gives minimum value(gradient descent) is done by scikitlearn?
towardsdatascience.com/minimizing-the-cost-function-gradient-descent-a5dd6b5350e1
Ma'am can we get regression for more than 2 independent variables w.r.t more than two dependent variable??
very nice presentation..
What do see as the advantage of using the sklearn's LinearRegression vs. statsmodels.OLS? The summary() and summary2() results tables from the latter are nice for seeing a whole lot of information at once.
Nice explanation 👍🏻
Thank you!
hello Megha, this is good one , easy for beginner ....kindly upload on clustering neural network also
thank you so much
You're welcome!
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.
Superb explanation madam thank you
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 :)
What happens if one of the variables has string values instead of numerical values?
Hello Megha. Great video. But you did apply the scaler function to standardize the days. Why?
After define x & y print statement should be came in " str " how it is possible if all dataset and format as it is copy .🤔
Very well done mam,why didn't you do outlier treatment mam ?
Thank you
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.
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.