💬 Join the Discord Help Server: link.alejandro-ao.com/981ypA ❤ Buy me a coffee (thanks): link.alejandro-ao.com/YR8Fkw ✉ Join the mail list: link.alejandro-ao.com/o6TJUl
Hello, great video just one comment is at 22:04 the reason it's recommended to convert it into a categorical type is that python/the model will treat it inherently as an int type which indicates that one is larger or greater than the other 1 > 0 which is not what we're looking for we want the model to treat it as if 1 is a yes and 0 is a no basically otherwise great content and i hope this helps
Great teaching! I am new to Python and ML and am learning a lot! How to handle if the predictor is categorical in nature, e.g. some Yes/No or 0/1 of something, but not a number/measurement. Can the logistic regression model handle that?
Great explainations, clear instructions and great work. I wish you could do more projects on other ML models as well. That would be really helpful. Thanks for this content man.
@@alejandro_ao thanks. I clarified a lot with your 2 videos of linear regression and logiatic regreasion. Thats why. Anyway, talking about genAI. Can you help with building a chatPDF app using a free LLM like groq
@@lasithdissanayake that's great to hear! absolutely, that is coming up very, very soon actually. i just need to finish putting together a course in genai that i will release in the next few weeks. but i should be able to put out that video within a couple of weeks 😎
unbelievable I learned a lot from you!!! Thank you so much! Cant wait to check your new tutorials, truly the best channel for beginners who wants to deep dive into AI! Is it possible that you can make a tutorial how to build an API around it or even how how to deploy it with e.g. Flask? (as you stated it in your conclusion) ❤
The Y variable is our target variable, so we have to be careful in not changing it's values because if we change them we can change the entire purpose of the model. Also, we normalize the independent variables to avoid "confusing" our model with a magnitude bias, the bigger the magnitude of the variable compared to the other, the bigger the bias in the training of the model so that's why we normalize, but for the target variable there is no need to normalize because the model Will predict the value, in this case 0 or 1, if we normalize the model would predict something different and to the length of my knowledge I don't think that we can interpret that correctly just yet (Sorry for the bad English) greetings from mexico ✌🏻
💬 Join the Discord Help Server: link.alejandro-ao.com/981ypA
❤ Buy me a coffee (thanks): link.alejandro-ao.com/YR8Fkw
✉ Join the mail list: link.alejandro-ao.com/o6TJUl
This video is highly educative. I wish he explains other ML algorithms in future videos. Thanks so much.
A standard scaler 30:00 transformers your values into a range of (-3 ; +3)
Thank u for the video.
Great explanation and real case example, thanks a lot
it's my pleasure!
thanks , the way you tackle each part of the project helps beginners like me learn and catch up easily
it's my pleasure! :)
You're really good at explaining everything. This is really a beginner friendly project where we can learn and understand. Thankyou so much Alejandro❤
I appreciate it!
Hello, great video
just one comment is at 22:04 the reason it's recommended to convert it into a categorical type is that python/the model will treat it inherently as an int type which indicates that one is larger or greater than the other 1 > 0 which is not what we're looking for we want the model to treat it as if 1 is a yes and 0 is a no basically otherwise great content and i hope this helps
This is one of the best videos on data science and I have seen a lot . Thank you for this. Please keep posting
I think its because the X variables are what we need for our predictions. The Y variable is just a result of the X variables
this is the best tutorial i have ever watched. thanks a lot man. And
Instead of train, test. is there any benefit of using train, validation, test?
Man You Did Awesome.. I can't buy coffee for you for now...but hope so in Future.. please continue building models
thanks! i will :)
@@alejandro_ao my class is over just now and we learned decision tree ... Please upload all models videos
keep it up bro
Great teaching! I am new to Python and ML and am learning a lot!
How to handle if the predictor is categorical in nature, e.g. some Yes/No or 0/1 of something, but not a number/measurement. Can the logistic regression model handle that?
Great explainations, clear instructions and great work. I wish you could do more projects on other ML models as well. That would be really helpful. Thanks for this content man.
it's my pleasure, mate. i am have been focusing much more on genai recently, but i'll try to make more regular ml content too!
@@alejandro_ao thanks. I clarified a lot with your 2 videos of linear regression and logiatic regreasion. Thats why. Anyway, talking about genAI. Can you help with building a chatPDF app using a free LLM like groq
@@lasithdissanayake that's great to hear! absolutely, that is coming up very, very soon actually. i just need to finish putting together a course in genai that i will release in the next few weeks. but i should be able to put out that video within a couple of weeks 😎
@@alejandro_ao great buddy. Thanks for the amazing content. Love from Sri Lanka ❤
informative and useful, you should make it a video on how to deploy it using flask or any other thing
thank you brother
you're welcome brother
Best video i have seen.. such an amazing explaination. can you please come up with more ml projects instead of langchain?
unbelievable I learned a lot from you!!! Thank you so much!
Cant wait to check your new tutorials, truly the best channel for beginners who wants to deep dive into AI!
Is it possible that you can make a tutorial how to build an API around it or even how how to deploy it with e.g. Flask? (as you stated it in your conclusion)
❤
Great work ...Thanks
thanks
it's my honour
Great video. Thank you.
Isn't you had to first split the data then normalized? the way you did would cause data leakage.
Great video. But I have a question. While wasn't the y variable normalized. Only the x variables were normalized?
The Y variable is our target variable, so we have to be careful in not changing it's values because if we change them we can change the entire purpose of the model. Also, we normalize the independent variables to avoid "confusing" our model with a magnitude bias, the bigger the magnitude of the variable compared to the other, the bigger the bias in the training of the model so that's why we normalize, but for the target variable there is no need to normalize because the model Will predict the value, in this case 0 or 1, if we normalize the model would predict something different and to the length of my knowledge I don't think that we can interpret that correctly just yet (Sorry for the bad English) greetings from mexico ✌🏻
why 42 for random state?
because it's the answer to the ultimate question of life, the universe, and everything , of course