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

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.

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

[cowjacketstudiostm]

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

[omarelliottgreen]

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

[tammu]

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

[vikrantyadav]

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

[mizanalisayed]

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

[bpeyyettipavankartikpra]

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

[special_]

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!

[CFDAsmaaHadane]

Thank you for this very informative tutorial! Please keep uploading

[shahzebmohammad]

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

[Deepsim]

I really like your well organized presentation structure!

[lookaway]

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

[resreng]

Thank you so so so much for clearing the concepts.

[ranulmbalasooriya]

Thank you so much for the clear explanation.

[samarthchakrawartiblogsand]

For a beginner this video is a big help!!

[sudarshandev]

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

[JawadAhmadKhan]

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

[shantanulokhande]

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

[yusiferzendric]

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

[hanifullah]

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

[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.