Machine Learning Tutorial Python - 8: Logistic Regression (Binary Classification)
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- Опубликовано: 23 июл 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 ?
I have never seen any other video explaining the concepts of machine learning so clearly. Keep up the great work..!!
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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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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You are the best teacher! I love the exercises at the end of each topic, which strengthens our understanding of what we learnt!!! Thank you so much! :)
I am glad it was helpful. :)
Thank you again! Great explanation! Always great tutorials!
Thank You Sir, I have learned a lot from your vids :). I was really perplexed by Logistic Regression and I am glad
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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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Glad you liked it :)
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Others making complicated like maths intuition.
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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.
Your videos are awesome. I'm learning so much!
For the first time after so many courses, videos, whitepapers, github, kaggle, exercises, wiki pages I am genuinely enjoying Machine Learning and I am doing all the coding and exercises by myself obviously after learning and understanding it all. Thanks a lot!!!
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.
Thank you so much for the graphical explanation...the concepts are crystal clear in my mind now.
Thank you, now if you have many features in your binary classification problem, and the classes overlap from visualizing data using pca, is it advisable to use Logistic Regression in this case?
I have no other words to say, the comments done by others have already conveyed my message to you!, Lots of love and thank you !
Sir Kindly confirm whether you already have a video for how to do Exploratory data analysis and feature selection. Thank you.
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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?
sir I have got an error "too many values to unpack" at 11:24,please help me to resolve this issue.
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Sunnny Singh, I am happy this was helpful to you
Any update you can give how's your data science journey is going as I am aspiring to be a data scientist..
one question though in the exercise given of employee retention why did not you dummify the dept variable and included in the features, cannot it be one of the important features which we should be including coz I have included it and got better score as well.
78 percent accuracy. I do all your exercises but in this I learned a lot. Thank you sir for such a great series @codebasics
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Now I learn machine learning....
Now What are you doing. I mean study or work
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Thanks
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.
Dear Sir
What a beautiful datasheet you have provided for practice with this video.
Spent more than two days to play with it.
Playing with the datasheet opened another dimension of the learning curve.
Thank you very much for providing relevant exercises like this as a challenge!
Happy that this is helping you Nilupul.
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Exactly what I was looking for, Thank You!
Perfect explanation on logistic regression.
Loved it. Thanks a lot.
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Thanks, sir .. your explanation is really clear and so easy to understand 👍🏼
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Glad it was helpful!
Building model means statistic formula will be the same but we use different columns of Data Frame for an output. Is it right sir?
Thank you very much ! Your videos are always my best choice to learn ML
Thanks a bunch, Subscribed here and also Liked all of the videos I've found so far from you. Best.
among several videos, this one is the best. appreciated
Rida, I am glad 😊
sir, in your given exercise can we drop the independent variable by backward elimination process ??
this video really really very helpful.
thank you so much for this amazing kwnldge
please make more video request.
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.
I have a question, how is the slope value updated for logistic regression when finding the best fit plane or line to seperate the 2 classes?
At first I thought gradient descent but that doesn't work because it is used to find the global minima
Maaeri aapahi hans di
Maaeri aapahi rondi
Maaeri yaad yaad wo aaeri
I like your tutorials very much, the explanation therein is superb and makes one understand even very hard to grasp concepts.
Suppose our target variable is in the form of categorical such as Yes & No. Then do we need to convert it as 1 & 0? Also, can we have a mixture of independent variable as categorical(string) and continous(numerical)?
just subscribed, your very good at explaining. thank you!
I am glad you liked it Pablo
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amazing tutorial!
Nice Sir, try to create SVM or PCA next with some mathematical explanation. thank you
Thanks one of the best tutorials !
Glad you liked it
awesome.... :) Sir, could you please make a video on how to detect and handle/dealing with outliers in model..? eagerly awaiting from you, haven't got any clarity on outliers.
Very nice and you present easiest way to understand. Thank you
thank you sir for your video.
Thanks a lot for the lucid explanation.
In the exercise, I got an accuracy of 77.2% in my model prediction.
Hi bro...
Now I learn machine learning...
What are you doing.... I mean study or work
one of the best explanation I've ever seen
God bless you and may He provide angles to solve all your problems. Thank you
Thanks a lot for this i was able to implement logistic regression after so many tutorials
Hi, thank you for the super helpful video, I wanna ask why in the solution, you didn't drop 1 dummy variable (i.e drop medium and leave high and low) ?
Bro it was easy and clean. Thanks!
clearly understandable explanation.
Thank you so much.
Very interesting and useful - well presented too
Thankyou very much for all help and support. Can you please make a video on mathematical explanation of sigmoid and logit function also ?
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Thanks a million.
ha ha .. nice. you made my day with this shower of praise Siddhant. Thank you for your kind words :)
thank you for the content, helped me a lot!
A very nicely explained tutorial, thanks for sharing this. Can you please do a video on model validation techniques or guide me to some simple and intuitive online resource for the same.
excellent explanation...thanks lot.. please make video on deep learning model using tensorflow and caffe...
great again! looking forward to your future videos!
what is penalty=l2 in the output 5 at 13:56 ? Can you please explain the parameters of the function
Well explained, Thanks!
You guys are life savers. man love your videos.
Amanullah, I am happy it helped you :)
it is one of the fantastic videos about Logistic Regression .. Many thanks
Hello Sir
actually I have one doubt
Suppose We have multiple columns in our dataset and from that we have to plot the graph using all values.
So shall we give all the column names as a parameter in the scatter()method
Please reply me
Awesome as always, thanks for everything!
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".
Thank you! So well done
thankyou for making videos, your content is great
Your way of teaching is very good. Thanks for the video ❤❤❤
Great explanation, I've understood everything, thanks!
Glad you found it helpful!
Sir,how can we improve accuracy of model the model that I built for employee retention dataset is coming as 77%
Feed the model more dataset and train it will improve accuracy
Great explanation. Can you please share the slides you have used? Going through slides make it faster to revise the concepts.
Very helpful!
is this possible to have 0 and 1 both for the same age and we can compute them further based on probability?
Hey plz tell me what to do when I have multiple Columns like age ,weight , bmi that i need to consider for prediction
Why do we write x and y arguments in split method? Is it because of syntax?
Thanks. It is really informative.
Hi, i am working on a Bank Churners project and want to predict if a bank customer is leaving the services or not. I am asked to test a few different machine learning models and i want to know if logistic regression is suitable for a prediction model using 4 independent variables. I am thinking of using SVM and Random Forest too. What is your opinion ?
Many thanks this is the first explanation that provides context and examples making its so simple to understand.
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].
Thank you Codebasicd for helping me understand Linear regression. I have question for Codebasics and everyone. please do well to answer me. thanks in advance.
I want to perform a logistic regression. I was asked to use state and political party and vote gotten as my independent variable and make a prediction whether a political party wins or loses. I have 36 states in my country and i want to use 3 dominant parties i want to use as a case study. my problem is how the layout of these data will be; I am unable to resolve party been in a separate column unless I take one political party and take one state and do the prediction explicitly and then move on to another.
Please i really needs you guys help to resolve these issue. Thanks in advance.