Multiple Linear Regression in Python - sklearn

Unlock the power of multiple linear regression using Python’s sklearn library with our step-by-step tutorial. This video is designed to help you master the art of predicting outcomes based on multiple variables. Learn how to set up your Python environment, import necessary libraries, and load datasets for analysis. We guide you through the process of fitting a multiple linear regression model, interpreting coefficients, and evaluating model performance with real-world examples. Whether you're a data science enthusiast or a professional looking to enhance your analytical skills, this tutorial provides clear, concise explanations and practical applications. Understand how to handle multicollinearity and improve your model's accuracy with tips and tricks from experts. Subscribe to our channel for more in-depth Python and data science tutorials, and elevate your ability to derive insights from complex datasets with multiple linear regression. Join us and start predicting with precision today!

If you are a complete beginner in machine learning, please watch the video on simple linear regression from this link before and learn the basic concepts first:

Here is the dataset used in this video:

Please feel free to check out my Data Science blog where you will find a lot of data visualization, exploratory data analysis, statistical analysis, machine learning, natural language processing, and computer vision tutorials and projects:

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#linearRegression #machinelearning #datascience #dataAnalytics #python #sklearn #jupyternotebook

[imveryhungry]

im glad people like you exist. I am simply not smart enough to have figured this out on my own

[anis.ldx]

Absolutely brilliant! Your way of explaining is beyond exceptional. Thank you so much for this simplistic explanation!

[souravdey]

Very good tutorial. No nonsense and clean. Thanks

[maheswaraardhani]

from the bottom of my heart, i want to thank you for your detailed and easy to follow explanation. i dont know who you are or where you are but you have my utter respect. big thanks

[albertjohnson]

I don't know who you are, but THANK you from deep heart for making this content

[muhammadaalimisaal]

I am kinda selfish type of person. Usually I donot like videos nor subscribe channels but how precise and to be the point your video was and I'm utterly impressed as this video was helpfull in clearning my concepts about MLR.
Goodluck, Best wishes. You have won a subscriber

[analyticalmindset]

I would've loved for you to squeak in a Residual analysis or whatever is done after you get your R2 values from your test and train group.

[ShouqAldosari]

thank you very much this helped me a lot hopefully, I will get a good grade !! :)))

[nevermind]

i think u can make a function to convert object name into numeric if the the data has many columns instead of writing 1 each 1 like this :
for column in df.columns:
if not
df[column] =
df[column] = df[column].cat.codes

df

[inamhameed]

Great video. Please can you share the insurance data? It's not visible in the description. Thank you

[RaihanRisad]

where can i get the dataset that you used

[programsolve]

Very well explained 🎉🎉
Thanks you so much 🎉🎉🎉

[PersonalOne-wnzd]

I have a Different Insight from that i used the Wine data set for that

[raymondkang]

Erm, I think the method you convert the data "region" is inappropriate. U cant convert the "region" as category since it become ordinal data. I think we should convert each of the region into dummy variables then we can see the coefficient of each region.

[Anand-]

could u plz provide the Dataset being used in the video

[Habbodonald]

Very good video. About the model, dont you need to check if R-square need an adjust to trust his income?

[tejallengare]

This video is very helpful thank you so much

[christophermiller]

Data isn't my background, but these videos help me understand how to structurally get there. Is there a way to export the predicted charges into a data population for further review. Also, is there a way to adjust the scatter plot dots by a filter on one of the independent variables (i.e. any record where age is 17, make the the plot red color). Thank you!

[JyotirmoyeeRoy]

Its showing a error as "df isn't defined "

[abbddos]

Good.. but normally we test a model with data that it hasn't seen before, and that's the test split.