49 - Logistic Regression using scikit-learn in Python

This tutorial explains the few lines to code logistic regression in Python using scikit-learn library.

[pranavpalaniappan]

I usually do not comment on videos but had to for this one. Wonderfully explained with step-by-step instructions. Thank you!

[hbale]

work man! The quality of your content and simplicity in explaining key concepts is very impressive. Keep up the awesome work!

[crane]

Love this video. This is the most explicit and practical tutorial on logistic regression in Python I've ever seen.

[samarafroz]

Great work best video for machine learning algorithm I've ever seen

[felip]

Thank YOU for your time and patience for the videos!

[vmdhar]

if the Visualization is also shown within this tutorial then it would be a wonderful explanation as you do always. Thank you for sharing

[mcb-ee]

Thank for the video.
What should we do if the dataset is divided %90 is 0 %10 percent is 1?

[jeanvaljean]

what if the output is not only "bad" or "good" but what if there's "normal" too? It isn't binary anymore. How can i deal with it please?

[kaushikgupta]

Sir, You explained these concepts in a best possible way! Thanks for helping us a lot .
Any suggestions for Beginners?

[obeynjanjeni]

Thank you sir, this is pretty good. an exceptional work indeed

[nahme]

I appreciate you ... the tutorials are really helpful

[bhavanasingh]

Hey, I am working on Google Colaboratory.
And this line of code Y = Y.astype('int') is not working.
kindly help.

[anasabdulla]

Can we do this method for multiple class classification problems? instead of 2

[baironmanuelvinez]

I think you can improve the prediction keeping user feature un the model using one hot encoding,

[Марсель-ис]

Great job man. i know about logistic regretion but not using model selection and train test imports.. Good to learn a quick way to make it
Some improvements on this code, find a way to show the sigmoid and the cost x iteraction graph.
edit: This code uses 100 iteractions as max number, wheres only 27 were needed. The Learning ratio or alpha, well i was looking for it, until realize that this is a Stochastic Average Gradient. Wich we can obtain the number, but we can't modify it..

[johnpuskin]

The result of Logistic Regression function is a real number within [0, 1]. Thus, you can set df.Productivity within [0, 1].
However, you set df.Productivty=2 in Line 25. It must be 0. Do I miss something?

[falfalkao]

Thanks for your nice work. May you show me what difference between random_state =20 or 1 or other numbers that are not None? Thanks

[judeleon]

Nice tutorial. However, instead you telling us go the previous tutorial, why not leave the link here, so it would be easy to find it. Or better still leave a link to the play list