I have watched endless Regression videos. I graduated with a math degree a couple years ago, and my programming skills are beginner level. I've done Datacamp stuff, youtube guided projects, you name it. This is by far the most concise, easy to follow, welcoming, calming video I have ever watched explaining how to do a regression project. I appreciate the quick definitions along the way, that help the audience including myself, not feel stupid lol. And explaining every small step really helps. Also the big picture explanation of how leverage this code and automate it, was helpful. Very few if any, ever talks about that step. I'm trying to get crack into the tech world and land a data job. Just this one video has truly given me the confidence, to not feel overwhelmed and that its obtainable. Sorry for the lengthy response haha Please keep making videos man, you rock!!
Wonderful video! I was able to follow along happily until the scatterplot command, for which I got the error, "TypeError: scatterplot() takes from 0 to 1 positional arguments but 2 were given". Had to change the command to sns.scatterplot(x=predictions, y=y_test). Everything else was perfect, and I learned a lot - THANK YOU!!!
Excellent video. Please keep doing such videos more and more. Just one thing was missing in the video, I guess. Finally, how to check the model with some random values. Here is the code: # Creating a DataFrame with random data random_data = pd.DataFrame({ 'Avg. Session Length': [33.8], # Example value 'Time on App': [12.0], # Example value 'Time on Website': [34.4], # Example value 'Length of Membership': [5.4] # Example value }) # Make predictions prediction = lm.predict(random_data) print(f"Predicted Yearly Amount Spent: ${prediction[0]:.2f}")
Today is Sept-5 and here we celebrate teachers day in india so I really want to thank you for making such videos and explaining every minute thing and complex things in such a simplistic way thank u so much dude
yo buddy,im also learning ml and just completed freecodecamp course but the sad thing is I learned theory mostly,not code so any suggestions?like how to start?i knew most of the things but in theory or you can say, i can understand code easily but couldnt write from scratch and for your knowledge, i havent watched this video yet...what should or where should I start now as I love ml,just having some trouble....
I've given you the 1000th thumbs up today, If I could I would have given you another 10000. You deserve more than that for your video really easy to follow and to undesrtand. I can wait to watch other videos of you.Thanks a lot👍
After watching a lot of videos for Linear Regression your video is by far THE BEST!!! thanks. Just one thing couldn't use scatterplot got a 'scatterplot() takes from 0 to 1 positional arguments but 2 were given' error.
The coefficient does not determine the most important. The magnitude of each variable varies in the formula. A small coefficient applied to a variable with more magnitude can be the most variance on the model
Hey. Nice video. There's just a thing I'm not sure about. Shouldn't normality, homoscedasticity and tests regarding residuals be done over y_train - predict, where predict is based on the training set? You verified the normality assumption on residuals of y_test - "predict_test". Which one is it?
I'm following 36:40 in the video but when I try to execute sns.scatterplot(predictions, y_test) I get an error: Scatterplot() takes from 0 to 1 positional arguments but 2 were given. Any solutions? I am using seaborn version 0.12.2 Could that be the issue?
For the first time in my entire 5yrs studying computer science, I understand and can explain linear regression and how it works. Thanks a Lot Sir.❤❤
you’re a legend, great job! keep it up 💪
I have watched endless Regression videos. I graduated with a math degree a couple years ago, and my programming skills are beginner level. I've done Datacamp stuff, youtube guided projects, you name it. This is by far the most concise, easy to follow, welcoming, calming video I have ever watched explaining how to do a regression project. I appreciate the quick definitions along the way, that help the audience including myself, not feel stupid lol. And explaining every small step really helps. Also the big picture explanation of how leverage this code and automate it, was helpful. Very few if any, ever talks about that step. I'm trying to get crack into the tech world and land a data job. Just this one video has truly given me the confidence, to not feel overwhelmed and that its obtainable. Sorry for the lengthy response haha Please keep making videos man, you rock!!
So wholesome! Thanks for helping me find the best video.
You're really underrated dude, this video was very well made especially for beginners interested in the field. Thank you so much!
thank you!!
I am from Rwanda. I would like to thank you very much for such explanation from this project
I'm a math major with a minor in CS. I'm looking to break into data science. Your video was very informative and easy to follow. Thank you!
you are literally in the best position to start off in the world of AI. enjoy the ride!
Amazing! Please keep doing these beginner friendly projects, theyre incredibly useful
Wonderful video! I was able to follow along happily until the scatterplot command, for which I got the error, "TypeError: scatterplot() takes from 0 to 1 positional arguments but 2 were given". Had to change the command to sns.scatterplot(x=predictions, y=y_test). Everything else was perfect, and I learned a lot - THANK YOU!!!
i got at that point too, thanks for the fix :))
Yeah me too then I searched and fixed it
Thank you!
Absolutely amazing. I am in engineering undergrad and required exactly what this video offered. Everything is perfect, keep up the good work. Cheers🍹
Great video! Here's a quick tip: consider using distplot to display Yhat and Y_test in the same axis to evaluate your model's performance.
Excellent video. Please keep doing such videos more and more. Just one thing was missing in the video, I guess. Finally, how to check the model with some random values. Here is the code:
# Creating a DataFrame with random data
random_data = pd.DataFrame({
'Avg. Session Length': [33.8], # Example value
'Time on App': [12.0], # Example value
'Time on Website': [34.4], # Example value
'Length of Membership': [5.4] # Example value
})
# Make predictions
prediction = lm.predict(random_data)
print(f"Predicted Yearly Amount Spent: ${prediction[0]:.2f}")
Such an underrated video. It was a pleasure to watch and follow along
Dude your explanation is far more better than anyone else and simple to understand i do not comment actually but i can say that u explain really good.
i really appreciate it! glad it was helpful :)
This is really awesome, it helped me to understand linear regression concept , love the way you teach through projects.
Thank you for this lesson, Alejandro: it helped me tremendously. Have a nice day and great holidays.
thank you! i'm glad to hear it helped. i hope you had great holidays :)
It is very helpful. Keep up with the great work!
Today is Sept-5 and here we celebrate teachers day in india so I really want to thank you for making such videos and explaining every minute thing and complex things in such a simplistic way thank u so much dude
yo buddy,im also learning ml and just completed freecodecamp course but the sad thing is I learned theory mostly,not code so any suggestions?like how to start?i knew most of the things but in theory or you can say, i can understand code easily but couldnt write from scratch and for your knowledge, i havent watched this video yet...what should or where should I start now as I love ml,just having some trouble....
@@rameezalipacific I am also new but i would say start doing or replicating real world projects from youtube
I've given you the 1000th thumbs up today, If I could I would have given you another 10000. You deserve more than that for your video really easy to follow and to undesrtand. I can wait to watch other videos of you.Thanks a lot👍
After watching a lot of videos for Linear Regression your video is by far THE BEST!!! thanks. Just one thing couldn't use scatterplot got a 'scatterplot() takes from 0 to 1 positional arguments but 2 were given' error.
yeah, I suggest you use the following instead :
plt.scatter(predictions, y_test, alpha=0.5)
it does the same thing, hope it helps
Put x= and y=. and it works
@@sakshamsharma708 Thanks bro for the suggestion! it worked.
you explain so well. I have learnt so much in this video. Going to use this in my project for sure. Thank you
nice to hear that! keep it up!
Wonderful tutorial! Learning with my dad, and we both enjoyed it :)
glad to hear this :)
looking forward to another END TO END video but for more intermediate level!
this is such a good beginner-friendly tutorial! thank u sm
hey there! it makes me very happy to hear that! keep it up ;)
the video I wanted to find..amazing content..keep working great things will happen.
i appreciate it!
Thank you so much sir ❤ please keep us updated in ml world like this ............
absolutely 🫡
Very Helpful. Thanks
you saved me with this, great job and thank you!!
you’re awesome 🙌
Great vid, thanks my man!
Amazing video ! for me as a beginner, that was very helpfull, thank you
Great to hear! Keep it up :)
Awesome tutorial, very helpful! Can I just check though, aren't the dependent variables supposed to be on y and the independent on X?
The coefficient does not determine the most important. The magnitude of each variable varies in the formula. A small coefficient applied to a variable with more magnitude can be the most variance on the model
Amazing video i learned a lot from you thank you for the job
Glad to hear it!
It is very helpful brother, Keep up good work!
Thank you, I will!
please make video on lasso,ridge,svm,knn,gradient boost etc..
big thanks for this video -- i learned a lot!!
Very welcome!
Thanks
Great work ! Im waiting for others ML algorithms.
thanks!! i’ll try to make more of this 💪
You are great brother.
u are the best
you are
thank you its great
very helpful, thank you
Hey. Nice video. There's just a thing I'm not sure about. Shouldn't normality, homoscedasticity and tests regarding residuals be done over y_train - predict, where predict is based on the training set? You verified the normality assumption on residuals of y_test - "predict_test". Which one is it?
Thanks!
thank you!
Really thanks it is very helpful
hey there, i'm glad it was helpful! keep it up :)
37:25 i can't plot the scatterplot why shoing error (scvatterplot() takes from 0 to 1 positional argument but 2 were given )
Hii, at the end of the prediction part, a problem occurs saying ValueError: Per-column arrays must each be 1-dimensional. Can somebody help me pls.
Can you tell me from where I can get this csv file ?
How can l download dataset from kaggle
I'm following 36:40 in the video but when I try to execute sns.scatterplot(predictions, y_test) I get an error: Scatterplot() takes from 0 to 1 positional arguments but 2 were given.
Any solutions? I am using seaborn version 0.12.2
Could that be the issue?
try this (sns.scatterplot(x=pridictions,y= y_test))