Machine Learning Tutorial Python - 8: Logistic Regression (Binary Classification)

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Finally i got perfect trainer for ML, your skills are excellence sir, we are very proud of you sir.

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Glad you liked it :)

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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.🥳

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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

[@anishagarwal71]

Could you pls tell me what exactly did you do to fine tune it?

[@ayushchauhan301]

If your testing data is so small then you can achieve accuracy 1 very easily ​@@anishagarwal71

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Perfect explanation with proper examples. Great job.

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[@codebasics]

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?

[@09_samarpanbasu7]

@@riyamitra8901 I think ...as logistic regression can handle multicollinearity between the dummy variables so it's not necessary to drop the last col.

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Perfectly balanced video. It forces anyone to continue to watch other videos of this series. Very well explained in simple language. 👌

[@manusingh9007]

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.

[@codebasics]

Great job manu. its a good score. Video description has a solution link, you can verify your code with mine.

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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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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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Sunnny Singh, I am happy this was helpful to you

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Any update you can give how's your data science journey is going as I am aspiring to be a data scientist..

[@adityabikramarandhara9477]

Thanks a lot for the lucid explanation. In the exercise, I got an accuracy of 77.2% in my model prediction.

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I never seen anyone explaining simple as like this. Others making complicated like maths intuition. Thanks code basics

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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! :)

[@codebasics]

I am glad it was helpful. :)

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amazing. astounding. bewildering. breathtaking. extraordinary. impressive. marvelous. miraculous. even all these adjectives are less to tell the quality of the video. Thanks a million.

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ha ha .. nice. you made my day with this shower of praise Siddhant. Thank you for your kind words :)

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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!

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Happy that this is helping you Nilupul.

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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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[@businnovate]

Why didn't you plot the sigmoid curve but only showed the scatter plot?

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[@hanselsamuel]

I really appreciate your tutorial videos, thank you. But how to find the "bought_insurance" values ?

[@michaelpeel3716]

Many thanks this is the first explanation that provides context and examples making its so simple to understand.

[@codebasics]

Glad you liked it Michael

[@engihabit]

@@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].

[@abhishekdobliyal7178]

Thank You Sir, I have learned a lot from your vids :). I was really perplexed by Logistic Regression and I am glad KZbin recommended this to me :)

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[@codebasics]

George I am glad 😊

[@mehmetkaya4330]

Thank you again! Great explanation! Always great tutorials!

[@codebasics]

Solution link for the exercise: github.com/codebasics/py/blob/master/ML/7_logistic_reg/Exercise/7_logistic_regression_exercise.ipynb Step by step guide on how to learn data science for free: kzbin.info/www/bejne/jJ_CnqCFqraeiaM Machine learning tutorials with exercises: kzbin.info/www/bejne/nZ7Zp5Sll9Jqm7M

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Thnaks a lot for theese amazing contents. I have just discovered your videos!

[@icyjump1684]

I only could get 77 with logisitic, but then i used lazypredict to find a higher accuracy and then used desicion tree classifier to get a 98, ty!

[@mario1ua]

Great explanation, I've understood everything, thanks!

[@codebasics]

Glad you found it helpful!

[@mageshs5726]

I got 78.833 accuracy value. In this exercise i had known lot of things thank you bro.

[@pranavnigam11]

I also got around ~78.5% using the average monthly hours and satisfaction level. Did you use the same features??

[@codebasics]

That’s the way to go Magesh, good job working on that exercise

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I love your series of videos as you are concerned with the student's learning! Thanks!

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Finally got the Python version of Andrew Ag's machine learning course. With a better explanation. thanks.

[@codebasics]

😊👍

[@leooel4650]

Awesome as always, thanks for everything! i got a 77% model accuracy based on the satisfaction_level

[@jsbean8415]

How did you get the prediction model accuracy by depedent variable? And 77% meaning is the probability that they will leave the company?

[@nxbil2397]

@@jsbean8415 model.score()

[@jsbean8415]

@@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".

[@shuaibalghazali3405]

Thanks a lot for this i was able to implement logistic regression after so many tutorials

[@asamadawais]

Dhavel you are excellent in explaining difficult concepts in very simple language!

[@codebasics]

I am happy this was helpful to you.

[@jugabrat6797]

Your way of teaching is very good. Thanks for the video ❤❤❤

[@suleimanachimugu9048]

0.84 accuracy score and 0.83 mean cross validation score. Thanks, Your tutorials have been helpful.

[@antimuggle_ridhi2565]

how?

[@mapa5000]

Thank you very much ! Your videos are always my best choice to learn ML

[@tariqahassan5692]

it is one of the fantastic videos about Logistic Regression .. Many thanks

[@0xN1nja]

one of the best explanation I've ever seen

[@userhandle-u7b]

Thank you so much, sir. I've got the score in the exercise 0.797. 🙂

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Hi, i found your course is truly enhancing the path towards Machine Learning concepts, kindly continue this and sir achieve a complete set of this machine learning course including all the kick start algorithms. Thanks Sparsh

[@codebasics]

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.

[@khalidfahim100]

I was doing the exercise and noticed something in the solution you provided. I think you need to drop one of the salary columns to avoid dummy variable trap. This was not done in the solution python notebook provided. Btw, excellent tutorial!!!

[@noorameera26]

This video is really really good. Love the way you teach, your pacing and all the things you mentioned are really useful. Thank u and may god bless u!

[@codebasics]

Glad it was helpful!

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Sir I tried this method, it is very easy to understand and use.. thank you sir

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God bless you and may He provide angles to solve all your problems. Thank you

[@fahadreda3060]

Another Great Tutorial, Thank you sir, Waiting for the next tutorial, keep up the good work

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Sir, I extremely appreciate your videos and efforts in teaching these things. Very helpful and great explanation!!

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Actually you are the best explainer

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very useful video.... you explain everything in a very simple manner. Thank you

[@codebasics]

Glad it was helpful!

[@MultiSpiros123]

Thanks one of the best tutorials !

[@codebasics]

Glad you liked it

[@PlayingCode]

Hey plz tell me what to do when I have multiple Columns like age ,weight , bmi that i need to consider for prediction

[@MohamedAshraf-zs6nv]

at 14:00 , why you used model.score(x_test, y_test), as you have to replace x_test with the prediction from the model to be like this: prediction = model.predict(x_test) model.score(prediction, y_test) then you can compare the prediction with the real value and so you can get the actual score

[@codebasics]

Score function will internally calculate y predicted from x test. After that it will compare y predicted with y test.

[@guneyfatma]

Why do we write x and y arguments in split method? Is it because of syntax?

[@boughrood]

Very interesting and useful - well presented too

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thankyou for making videos, your content is great

[@abdulmuzakir]

Thanks, sir .. your explanation is really clear and so easy to understand 👍🏼

[@study_with_thor]

Perfect explanation!

[@codebasics]

Glad it was helpful!

[@reemnasser9105]

Mathematics make sense with you now. Finally, I can understand many concepts that I failed to understand them in university. I would just like to point out that many duplicates exist in this data. I would also like to ask why didn't we apply Label encoding for this model?

[@prakritreeeeeee]

Thank you so much for the graphical explanation...the concepts are crystal clear in my mind now.

[@imaansarwar2314]

thank you so much ! you have helped alot in learning the algorithms. Saved time with such a quick and easy way of explaining as I didn't have time for my fyp compleion and these videos are saving my time to get an idea of all algorihtms

[@flyingsalmon]

I have an interesting problem for you that I think you'll really enjoy sharing in a video. We would like to know your approach to solving the following problem: We have a soccer (euro football) game where there are penalty picks. We have data on who's the shooter/kicker and who's the goalie, and how many historically have been saved or not saved by the goalie as follows: (Assume the Shooter and Goalie letters are names of players and Saved is 0 (goal not saved, saved) Shooter Goalie Saved A Z 1 B Z 0 A Z 0 B Y 0 A Y 1 .... and so on for various rows Challenge: What we want to know is if shooter B is facing goalie Z, will the goal be saved or not? We want to leverage machine language (not just probability calculation, which can be done manually based on data). How should we solve this? Many thanks in advance! If you make a video on such, I'll even donate :) Promise!

[@devanshgoel9070]

Thank you sir for this amazing explanation of Logistic Regression.

[@codebasics]

Glad you liked it

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Exactly what I was looking for, Thank You!

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I like your tutorials very much, the explanation therein is superb and makes one understand even very hard to grasp concepts.

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You are the best teacher

[@lightonkalumba]

lol i just love the part on the exercise when he sayed you are a data scientist.

[@pagnonig]

Your videos are awesome. I'm learning so much!

[@ChiTamNguyen-d9d]

At 8:00, when converting the linear regression line to the logistic regression line, I think the point where y = m*x + b = 0 (left corner) is also the point where the y = 1 / 1 + e^-(m*x + b) = 0.5 (inflection point). Why are the two graphs are different? Or am I missing something?

[@Christian-mn8dh]

just subscribed, your very good at explaining. thank you!

[@codebasics]

I am glad you liked it Pablo

[@balasubramanian5232]

Sir Kindly confirm whether you already have a video for how to do Exploratory data analysis and feature selection. Thank you.

[@ravisrivastava2909]

Hello Sir! Great work by you. there is a problem in your code may be due to version of python .... If we use X_train, X_test, y_train, y_test = train_test_split(df[['age']],df[['bought_insurance']],train_size=0.9) then only we get score of 1 otherwise with your code it is 0.66.

[@Myanmartaeyang]

OMG. I was trying to run the code and did not know why mine was 66%. Thank you for this !!!

[@pablu_7]

Reasons behind the employee retentions are: 1.Satisfaction Level 2.Average Monthly Hours 3.Promotion Last 5 Years

[@nxbil2397]

Noted that

[@MrPrudhvisai]

why not work accident?

[@arshdeepsingh8289]

@@MrPrudhvisai work accident should be there

[@amanullahmahabub78]

You guys are life savers. man love your videos.

[@codebasics]

Amanullah, I am happy it helped you :)

[@Yang11235]

Hi, sir, i had view your answer in exercise.I think may be forget to remove a dummy column from dummies, It may be lead to dummy variables trap, because of regression model can auto drop dummy column so you run model successfully. but not all model can auto drop dummy column , You once advised me that was not a good habit🤣

[@flavio4923]

I'm reading books to try to understand some of this, and I'm impressed at the ease of the examples and explanations in your video. O_O gotta watch more

[@DimanjanDahal]

same here

[@gusinthecloud]

The best explanation as always

[@codebasics]

Glad you liked it

[@krishkonnect814]

Thankyou very much for all help and support. Can you please make a video on mathematical explanation of sigmoid and logit function also ?

[@tayfundogruer8026]

Bro it was easy and clean. Thanks!

[@micro_Dots]

clearly understandable explanation. Thank you so much.

[@ridael-mehdawe4681]

among several videos, this one is the best. appreciated

[@codebasics]

Rida, I am glad 😊

[@PolkadOfficial]

Very well explained, thanks!

[@codebasics]

Glad you enjoyed it.

[@flavioquaresmaa]

Good job. Thanks for teach me. In Portuguese, this content is not yet widely explored. 👍

[@codebasics]

hey Flavio. I am glad you liked it :)

[@anselmtuachi3298]

class example: 2+2 = 4 homework: if 3+3 = 6, calculate the mass of the sun😂😂 i'm really grateful for this tutorial though its been really great till this point

[@shylashreedev2685]

Thank u so much..it really helped to clear my concepts

[@narsinhakulkarni9167]

Sir .....I cannot thank you enough!!!......thanks for introducing kaggle as well.

[@codebasics]

Thanks narsinha

[@sanciafernandes3335]

Great explanation

[@codebasics]

Thank you! I'm glad you found the explanation helpful.

[@harshvardhanpardeshi3584]

for anyone facing problem in using the df.groupby().mean() function, just drop the non numeric columns. It'll work :)

[@JunaidAnsari-my2cx]

Tysm It worked

[@Hari983]

Very well done and explained even for beginners - thank you so much!

[@bandhammanikanta1664]

Perfect explanation on logistic regression. Loved it. Thanks a lot.

[@anand.prasad502]

medium.com/trainyourbrain/would-you-read-this-article-or-not-b757d0e26cf8

[@saranyachimirala8351]

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.