Quick extra note for anyone since I was stuck on it for a bit. If you have any columns you want to exclude beforehand (for me, it kept picking up data with string values too and I wanted to exclude those), run this first for those columns before defining x and y: data_df.drop(['Column1', 'Column2', 'etc..'], axis=1, inplace=True) Using "inplace=True" will make it so those columns will stay out of the dataframe because if you define it as "inplace=False" or don't define it at all, those columns you removed will go back into the dataframe anyway. It wasn't used in defining x and y because we need the column PE to return to the dataframe. And thank you so much for this video miss Megha. I'm new to Python and have been struggling with this for an assignment for hours and this really helped me.
Hi Megha...the explanation is neat and clean...right at the point. Very beautifully explained and the concept is clear. Can you please upload more videos on Logistic Regression, KNN, Random forest, Support Vector machines, Decision tree etc?
Hi Megha, you are a mega tutor indeed. You're more than awesome. Without any gaining, this is the best explanation and best perspective I have come across on youtube regarding ML. You're superb ma'am. I await more of your uploads. Thank you!
Hi, your videos are awesome and easy to understand. Can you please upload the logistic regression, random forest, SVM and times series modeling videos with examples.
@@jaydeepraut820 did you try to search in Google for the dataset described, as she showed and explained it at the video timestamp @1:10 and following? As she describes this in the video, it is straightforward to search and find from Google.
I really appreciate your work, however, I will like to point something I noticed out. The scatter plot you created for your result at the end of the analysis where you had the y_test plotted against the y_pred seems inaccurate to me. plt.scatter(y_test, y_pred) is supposed to indicate that your y_test is on the x-axis while your y_pred. is to be plotted on the y-axis. I believe what you should do is showcase the y_test and y_pred on the y-axis while you use a common x-axis for the two on the same plot.
Two different ways of showing the same basic thing. y_pred vs. y_test will form a 45-degree straight line if the data is perfectly predicted, whereas y_test and y_pred both vs. x_test will show the points overlapping each other, if perfectly predicted. However, because this is multiple regression, there isn't a single x variable, so you would have several of the latter plots. These are useful for certain diagnostics. However, a very common first plot is y_test vs. y_pred. Plotting residuals vs. y_test or x_test (one at a time) are also common charts to make.
how to perform regression for the following equation: time= ( pressure)^a + (concentration of fuel)^b + (concentration of oxidizer)^c + exp(d/Temperature). Here, time is dependent variable on pressure, concentration of fuel, concentration of oxidizer and temperature. How to set non-linear model like the above equation
Could you please make a video on this topic from scratch without using the sklearn library to better understand the mathematics behind it or provide a useful link? Please...
@Chandra Shakhar start with the website she shows in her discussion of sklearn: scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html , which leads you to the technical explanation at: scikit-learn.org/stable/modules/cross_validation.html#cross-validation
Brilliantly explained. Can u make video for deployment of model to use with webpage/android or any programming language GUI through API . Also make such a beautifully explained video for ANN also.
Fantastic tutorial. Question I have is if I wanted to test my model on another dataset how could I do it once I have my coefficients and intercept? Best Wishes Peter
![KSI - Thick Of It (feat. Trippie Redd) [Official Music Video]](http://i.ytimg.com/vi/At8v_Yc044Y/mqdefault.jpg)
Literal queen. Been crying for a week over this. I could've just watched this, this is amazing.
After 6 hours of stumbling through StackOverflow and various books, this video made it clear in 20 minutes!
Thank you SO MUCH!!!
Since 2 days i am trying to understand ML. Finally abhi ache se samajh gaya. Thanks
Beginning my ML journey. Thank you for the crisp explanation.
Quick extra note for anyone since I was stuck on it for a bit.
If you have any columns you want to exclude beforehand (for me, it kept picking up data with string values too and I wanted to exclude those), run this first for those columns before defining x and y:
data_df.drop(['Column1', 'Column2', 'etc..'], axis=1, inplace=True)
Using "inplace=True" will make it so those columns will stay out of the dataframe because if you define it as "inplace=False" or don't define it at all, those columns you removed will go back into the dataframe anyway. It wasn't used in defining x and y because we need the column PE to return to the dataframe.
And thank you so much for this video miss Megha. I'm new to Python and have been struggling with this for an assignment for hours and this really helped me.
Amazing video! This is my first project ever and I hope to continue further in my career of Data Science!
Simple, Clear, Concise. What else do you want?
Thank you!
This is the best tutorial i have come across ... simple ,easy and beautiful.
Please upload other regression and classification problems.
Thank you for this very informative tutorial! Please keep uploading
one of the best explanations in very simple words... Bravo Miss Megha Narang
In wish I can give this video a million likes.... thank you very much.. this video was really helpful.
Thanks sist, you help me to understand about a long code becomes a short code. It's a smart video.
This is sooooo Great!!!
Thank you so much for the clear explanation.
For a beginner this video is a big help!!
I only had difficulty in plotting the model, thanks a lot 😃😃👍👍
This is a very good video! Thank you very much!
you are life saver
Thank you so much for the video!
Thank you for the visualization.... :)
Thank you so so so much for clearing the concepts.
Thank you so much! Super Clear!
Glad it helped!
I liked the video, so I "Liked" and I "Subscribed"
Thanks, MN
you are the best!!
thank you so much this video help me to understand the concept faster
Glad to hear that!
Wow really its very easy to understand, Mam your sequence wise explanation is awesome.
Thank you so much. It was really helpful!!!
Awesome video. keep on doing great
So good! this is much simpler explanation. I love it
Great explantion ............ur work must be admired
Really helpful tutorial! Thank you.
Hi Megha...the explanation is neat and clean...right at the point. Very beautifully explained and the concept is clear. Can you please upload more videos on Logistic Regression, KNN, Random forest, Support Vector machines, Decision tree etc?
Hi Megha, thank you so much for the video! It helped me a lot in work.
Really appreciate! hope you keep making that
Thank you
Amazing tutorial!
Very clear and to the point . . . .kindly make similar videos for each topic such as Decision tree classifier etc
Hi Megha, you are a mega tutor indeed. You're more than awesome. Without any gaining, this is the best explanation and best perspective I have come across on youtube regarding ML. You're superb ma'am. I await more of your uploads.
Thank you!
So useful!!! Thank You
Very meaningful session, great explanation 👍
Great Ma'am
Simply Brilliant !!
Excellent!
very nice and easy teaching. Congrats.
Thanks!
I really like your well organized presentation structure!
GREAT WORK MEM👏👏👏👏👏
Excellent💯👍
Cleannn tutorial, the best of all, thx :))
Very nice explanation. Thanks!! for the explanation
very insightful many thanks for your impressive work
Clear and Nice explanation....
Thank you
Should always perform a check for assumptions of Linear Regression when performing it. Otherwise, it can be dangerous or misleading.
Do you have any good material for guiding how to check those assumptions using the exercise presented in the video?
simple explanation... Thank you mam
Thanks @Dhanesh
thank you miss
Great breakdown! Liked and Sub 👌
Very nice video. Thank you so much and Best of luck--Shakir, Bangladesh
Thank you so much. This is very helpful.
Great explanation mam...just simple and smooth 😃..keep uploading videos
nice practical
brilliant video
THANKYOU GREAT VIDEO
Thanks a lot..this video has helped me a lot in my project❤❤❤❤
Glad I could help :)
Hi, your videos are awesome and easy to understand. Can you please upload the logistic regression, random forest, SVM and times series modeling videos with examples.
sure, will try to upload something soon
Thank you very much, and please upload with the dataset available on the internet, so that we can try on our own.
@@jaydeepraut820 did you try to search in Google for the dataset described, as she showed and explained it at the video timestamp @1:10 and following? As she describes this in the video, it is straightforward to search and find from Google.
Nice explanation 👍🏻
Thank you!
Hi Thanks for the informational video, it is very easy to understand.
Very useful
Do you have a guide on how to do multiple variables if they are non linear? Meaning we’d have to use a polynomial method with degrees?
Mam plzz further model bi explain kardo...
Waiting for your further ML lecture mam
Hello Megha. Great video. But you did apply the scaler function to standardize the days. Why?
Thank you for your nice explanation, can you upload more videos other modelling, please? thanks you mam
Thank you!
thank you so much
You're welcome!
thanks for this video
Keep it up and make videos on other models too mam.
I really appreciate your work, however, I will like to point something I noticed out.
The scatter plot you created for your result at the end of the analysis where you had the y_test plotted against the y_pred seems inaccurate to me. plt.scatter(y_test, y_pred) is supposed to indicate that your y_test is on the x-axis while your y_pred. is to be plotted on the y-axis. I believe what you should do is showcase the y_test and y_pred on the y-axis while you use a common x-axis for the two on the same plot.
Two different ways of showing the same basic thing. y_pred vs. y_test will form a 45-degree straight line if the data is perfectly predicted, whereas y_test and y_pred both vs. x_test will show the points overlapping each other, if perfectly predicted. However, because this is multiple regression, there isn't a single x variable, so you would have several of the latter plots. These are useful for certain diagnostics. However, a very common first plot is y_test vs. y_pred. Plotting residuals vs. y_test or x_test (one at a time) are also common charts to make.
Superb explanation madam thank you
Thank you so much ❤️
simply awesome
very nice presentation..
Hello I have this problem how can I solve it?
(could not convert string to float: 'rainy')
how to perform regression for the following equation: time= ( pressure)^a + (concentration of fuel)^b + (concentration of oxidizer)^c + exp(d/Temperature). Here, time is dependent variable on pressure, concentration of fuel, concentration of oxidizer and temperature. How to set non-linear model like the above equation
Nice video mam .. God bless you ☺️
Hi thank you for sharing. But I am wondering how do you get the actual linear regression equation with sklearn
Gracias, me ayudo mucho tu video :)
Mam I got an error ln[8]
Could not convert string to float:AT
How to fix it? Can anyone tell me?
Could you please make a video on this topic from scratch without using the sklearn library to better understand the mathematics behind it or provide a useful link?
Please...
@Chandra Shakhar start with the website she shows in her discussion of sklearn: scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html , which leads you to the technical explanation at: scikit-learn.org/stable/modules/cross_validation.html#cross-validation
Nice video
What keyboard are you using? It sounds so soothing.
@ Megha, thank you for this. Have you done another video of a way to improve the model? If so, can you kindly share the link?
thanks a lot, please I want to know which algorithm is used (batch, stochastic...?) also can we show the cost function?
👌👌👌
Brilliantly explained. Can u make video for deployment of model to use with webpage/android or any programming language GUI through API . Also make such a beautifully explained video for ANN also.
Very nice explaination. Could you please tell how we can get equation for the predicted model?
@Megha thank you so much for clear explanation. Much appreciated. can you pleased help to create video on how to improve model further.
sure, I will try to upload a new video soon
hello Megha, this is good one , easy for beginner ....kindly upload on clustering neural network also
amazing
Thanks
Very well done mam,why didn't you do outlier treatment mam ?
After define x & y print statement should be came in " str " how it is possible if all dataset and format as it is copy .🤔
Could you please upload non-linear regression?
If we have two outputs in multiple linear regression model. What are the steps ?
Very Nice Megha..Just if you give some explanation of functions which are using then it will be more clear. Nice Attempt!!!
Fantastic tutorial. Question I have is if I wanted to test my model on another dataset how could I do it once I have my coefficients and intercept? Best Wishes Peter