Linear Regression Analysis | Linear Regression in Python | Machine Learning Algorithms | Simplilearn

This Linear Regression Analysis video will help you understand the basics of linear regression algorithm. You will learn how Simple Linear Regression works with solved examples, look at the applications of Linear Regression and Multiple Linear Regression model. In the end, we will implement a use case on profit estimation of companies using Linear Regression in Python.

Below topics are covered in this Linear Regression Analysis Tutorial:
1. Introduction to Machine Learning
2. Machine Learning Algorithms
3. Applications of Linear Regression
4. Understanding Linear Regression
5. Multiple Linear Regression
6. Usecase - Profit estimation of companies

What is Linear Regression Analysis?
Machine Learning is an application of Artificial Intelligence (AI) that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed. Linear regression is a statistical model used to predict the relationship between independent and dependent variables by examining two factors:
Which variables, in particular, are significant predictors of the outcome variable?
How significant is the regression line in terms of making predictions with the highest possible accuracy?

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✅ Skills Covered
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- Descriptive Statistics
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[gochasethesunset]

When your uni teacher says to google things to answer your questions instead of teaching you but this video has my back haha thankyou!

[maitreyverma]

Use "annot" = True parameter in the sns.heatmap() to show the numerical values as well. Makes it much comprehensible.

[AryaInk]

How can I like this more then once ?
The easiest explanation without wasting any time.

[SimplilearnOfficial]

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Do you have any questions on this topic? Please share your feedback in the comment section below and we'll have our experts answer it for you. Also, if you would like to have the dataset for implementing Linear Regression in Python, please comment below and we will get back to you.
Thanks for watching the video. Cheers!

[sherenemukherjee]

Best explanation for regression so far! Most training videos only focus on the code part, which leaves people thinking about the mathematical deductions behind regression. But this covered it all.

[alonhoresh]

Great video BUT do mind the mistakes:
% is percent (not ampersand) and panda.dataframe.corr() is for correlation, not coordinates,
In the multiple regression slides, you show ONE variable 'c' and you call it coefficient but sklearn coef. are the slopes, you demonstrate it when you print regressor.coef_ (call 'c' in the slides constant and not coefficient).
you do such a good job explaining, these little things ruin some of the fun.

[johnanih]

I wish I could like this more than once. The best so far! Excellent job!

[ariannarisya]

Hello. When I did sns.heatmap(companies.corr()), I have error: ValueError: could not convert string to float: 'New York'. I followed all the steps. Thanks

[mattpurcell]

Great video, but when I replicate this for a class it is using an older version of sklearn and says categorical_features has been deprecated. I'm attempting to use ColumnTransformer with mixed results with the provided data. Can you point me in the right direction?

[andersonrojas]

the complexity of the subject matter becomes easy and simple to learn! thank you very much

[iujlibm]

No way to express my gratitude. Amazing explanation with code. I don't how I missed this video for long time

[urbantech]

19:23 I think that's column not row..!

[asimuddin]

Hello! This video is very helpful to understand the basics of Linear Regression but can you update the code where you import sklearn.preprocessing to transform the State column? Since the latest sklearn library removed categorical_features and hence we are getting errors as "TypeError: __init__() got an unexpected keyword argument 'categorical_features", Thank you!

[olawaleonafeso]

Loved this so much. Understood the contents perfectly. Thank you

[nineteen]

Great video, wish I could meet the teacher and say thank you in person... Truly-knowledge is free if you wanna simply learn....

[danieldesantiago]

I don't understand why you did not just put the dataset in the description box.

[Anwin]

Great video! 🤗 I'm super glad that anyone who wishes to learn whatever tech or skill, they've the opportunity to learn from this amazing community! Many Thanks, #Simplilearn!❤

[DidaKusAlex]

Hi, i have to say Amazing video, I had seen a lot of videos looking for a easy way to learn, but this video is the best, I can get it! I think you should update the code because the part "categorical_features" doesn't work.

thanks for shared this information and learn us about ML.

[justelmij]

that was the best explanation I had so far to understand Linear regression, thank you sir.

[felipejimenezortega]

Thanks for doing it for free, I hope I can use it in my practice work