Machine Learning in Python: Building a Linear Regression Model

In this video, I will be showing you how to build a linear regression model in Python using the scikit-learn package. We will be using the Diabetes dataset (built-in data from scikit-learn) and the Boston Housing (download from GitHub) dataset.

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[adir]

Hello Prof, I want to thank you for putting together training videos like this one. I have learned more than i have in the last 2 months of my data science MSc programme. You explained every line of code, every symbol and the reason behind every style of coding, that is what is called knowledge impartation. Thank you very much.

[akbaraliotakhanov]

Thanks so much, this is what a Linear Regression actually is and how we apply it into our dataset.
Pls also make videos about how applying Logistic Regression, KNN, Random Forest, SVM, Naïve Bias, Decision Trees using Python into our dataset.
Very interesting and clear

[edpalen]

every video you have posted provides value to the audience. Outstanding job. I hope your channel could grow exponentially, as it is deserved.

[CapitanJusticia]

Nice video, but how do we interpret the results? IOW, what would be the deliverable to our stakeholders? What are the actual predictions?

[dca]

This was great thank you so much! Really useful and looking forward to using it in my research.

[ramblingman]

You are the best professor for explaining, thanks for your content!

[srinivasmalvadkar]

this was realy helpful and wonderful of all other

thank you so much sir

[_GayatriShetkar]

Can we call it a multiple regression model?
As we're predicting a value considering multiple parameters

[Data_Man]

Just found your channel. Thank you from a fellow 🇹🇭

[ShoaibKhan-okiu]

Perfectly well put together videos. Just a little request about the linear regression model performance part can you elaborate a little bit what those numbers really mean. is this model good or bad?

[marcofestu]

Glad to have more of your video to watch than usual 😍

[HealthOnMyMind]

This is exactly what I was looking for, thank you so much this was such a big help!!!

[titiQd]

hi @Data Professor, I can i ask for minutes (9.50 - 10.00) when you explain about modulo operator. So i confused with the 0.523810833536016 where is that number come from? i keep repeating and repeating your video but still don't get where that float number comes up. at moment i do some assignments/ project and use your YT tutorial as guidance for me grasp this linear regression. thank you

[zoro]

your way of describing is really helpful to me. Thanks a lot for your videos.

[michaeloladunjoye]

Beautiful presentation. Thank you sir.

[tommytan]

I am lazy to comment usally, but this video is very delicious . Keep up with the good work, just subscribed.

[ektasingh]

Thank you for making this video. It is very helpful. 👍

[RM-lbxw]

Great video, looking forward to more such videos like these. Also, can you tell me what R2 score tells us about the model?

[matthewjaworski]

Hello. I am confused about what data is being held in X_train and Y_train. I have only done linear regression with 2 variables before and I am confused about why a 353x10 matrix is being held in X_train and why a 353x1(?) matrix is being held in Y_train. Is Y_train a placeholder for 353 regression line y values that get produced after the 10 variable coefficients are calculated and made into a function? Or is the algorithm solving an overdetermined system of 353 equations with 10 unknowns using linear algebra: (y1=b0 + b1x1...) . . . (yn=b0 + bnxn...)?

[dr.navidsoltani]

If one trains on 100% of the data (skipping split/train/test), does the sklearns lin/logreg-implementaiton basically become the same 'classic' implementation as statsmodels or glm (in R)?