49 – Logistic Regression using scikit-learn in Python

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

The code from this video is available at: https://github.com/bnsreenu/python_for_microscopists

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Comment List

• DigitalSreeni
January 9, 2021

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?

• DigitalSreeni
January 9, 2021

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

• DigitalSreeni
January 9, 2021

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

• DigitalSreeni
January 9, 2021

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?

• DigitalSreeni
January 9, 2021

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

• DigitalSreeni
January 9, 2021

Thank YOU for your time and patience for the videos!

• DigitalSreeni
January 9, 2021

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

• DigitalSreeni
January 9, 2021

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..

• DigitalSreeni
January 9, 2021

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

• DigitalSreeni
January 9, 2021

Nice step by step explanation 🙂

• DigitalSreeni
January 9, 2021

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