Machine Learning Tutorial Python - 8: Logistic Regression (Binary Classification)
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- čas přidán 23. 06. 2024
- Logistic regression is used for classification problems in machine learning. This tutorial will show you how to use sklearn logisticregression class to solve binary classification problem to predict if a customer would buy a life insurance. At the end we have an interesting exercise for you to solve.
Usually there are two types of machine learning problems (1) Linear regression where prediction value is continuous (2) Classification where predicted value is categorical. Logistic regression is used for classification problems mainly.
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Code: github.com/codebasics/py/blob...
Exercise: Open above notebook from github and go to the end.
Exercise solution: github.com/codebasics/py/blob...
Topics that are covered in this Video:
0:00 - Theory (Explain difference between logic regression and classification)
1:18 - What is logistic regression?
1:26 - Classification types (Binary vs multiclass classification)
1:53 - Explanation of logistic regression using the example of if person will buy insurance based on his age
5:38 - Sigmoid or Logit function
8:18 - Coding (for coding we are using an example of if a person will buy insurance or not based on his age)
14:36 - sklearn predict_proba() function
15:49 - Exercise (Solve a problem of predicting employee retention based on salary, distance to work, promotion, department etc)
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I really appreciate your tutorial videos, thank you. But how to find the "bought_insurance" values ?
One of the few videos that clearly shows the training data that the model is attempting to fit to. Thank you.
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Glad you liked it :)
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Solution link for the exercise: github.com/codebasics/py/blob/master/ML/7_logistic_reg/Exercise/7_logistic_regression_exercise.ipynb
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I am glad it was helpful. :)
Thank You Sir, I have learned a lot from your vids :). I was really perplexed by Logistic Regression and I am glad
CZcams recommended this to me :)
This video is fantastic. I'm teaching myself machine learning and this was one of the most helpful resources I've found online. Excited to watch/work-through the rest of the videos! Thank you so much
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Others making complicated like maths intuition.
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Actually, I fine tuned my model and was able to achieve an accuracy of 1.0. Thankyou so much sir. This might just be the best channel I have seen.🥳
Hi, I have some slight problem. How can I plot the prediction curve after training my model? Would be glad if you reply. Thanks
Could you pls tell me what exactly did you do to fine tune it?
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Perfect explanation on logistic regression.
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Glad you like them Riya and I wish you all the best! I have many playlists and recently left my job to focus on online teaching. My goal is to produce even a better quality tutorials then this.
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@@codebasics One question here. Why we did not remove one of the dummy variable after dropping salary column in Logistic regression like we did for Linear?
@@riyamitra8901 I think ...as logistic regression can handle multicollinearity between the dummy variables so it's not necessary to drop the last col.
Thanks one of the best tutorials !
Glad you liked it
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Sparsh
Thanks for appreciation Sparsh. I am continuing the series, it is just that due to my schedule I am not finding lot of time to work on it but I will try my best to speed up new tutorial additions.
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Glad it was helpful!
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clearly understandable explanation.
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Happy that this is helping you Nilupul.
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In the exercise, I got an accuracy of 77.2% in my model prediction.
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What are you doing.... I mean study or work
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Thanks a lot for this i was able to implement logistic regression after so many tutorials
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On first attempt, i considered 'left' as dependent variable and everything else including salary and department as independent variable, got 77% score of accuracy. Thanks for the wonderful video.
Great job manu. its a good score. Video description has a solution link, you can verify your code with mine.
God bless you and may He provide angles to solve all your problems. Thank you
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Glad you liked it Michael
@@codebasics 15:35 I can’t execute it??
model.predict(57) and any number like 25, 60 got the following ValueError: Expected 2D array, got 1D array instead:
array=[57].
Dhavel you are excellent in explaining difficult concepts in very simple language!
I am happy this was helpful to you.
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This video had good information, it was really helpful. I am still a learner, new to this field. I understand how to write and basics of confusion matrix using binary classification. But some terminologies are confusing. Can you please explain what exactly are base rate, test incidence, conditional incidence, classification incidence? That would be appreciated.
Finally got the Python version of Andrew Ag's machine learning course. With a better explanation.
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i got a 77% model accuracy based on the satisfaction_level
How did you get the prediction model accuracy by depedent variable? And 77% meaning is the probability that they will leave the company?
@@jsbean8415 model.score()
@@nxbil2397 that will show you the overal accuracy of your model. My question is , how you will get the probablity % that the employee will leave given the dependent variables? Like the one you have mentioned "satisfaction level".
Sir I tried this method, it is very easy to understand and use.. thank you sir
Perfect explanation!
Glad it was helpful!
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Thank you so much, sir. I've got the score in the exercise 0.797. 🙂
Thank you so much....very good explanation
Glad it was helpful!
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Actually you are the best explainer
Thank you sir for this amazing explanation of Logistic Regression.
Glad you liked it
You are the best teacher
Nice Sir, try to create SVM or PCA next with some mathematical explanation. thank you
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Glad it was helpful!
Thanks a lot Sir!!
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Awesome tutorial
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Thankyou very much for all help and support. Can you please make a video on mathematical explanation of sigmoid and logit function also ?
The best explanation as always
Glad you liked it
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