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For Stock Price predictor can day be Independent variable and Price be Dependent variable. Is Linear regression fit for Stock Market Price predictors??
The main difference between them is that the output variable in regression is numerical (or continuous) while that for classification is categorical (or discrete). However, they both are categorized under the same umbrella of supervised machine learning.
If the goal is prediction or forecasting or error reduction, linear regression can be used to fit a predictive model to an observed data set of y and x values.
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You are a world-class instructor, your speaking skills are really good. Even I had completed the video in 2x speed, I did not find any place where I have to replay the part. The linear regression algorithm is explained in the easiest possible way. Thanks for the effort!!!!
Thanks for creating this video, was looking for a simple explanation, had wasted 3-4 days finding good video, finally got this, thanks again. Was trying to follow along on excel sheet, I wish graphs were plotted in google sheet or excel.
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We are super happy that Edureka is helping you learn better. Your support means a lot to us and it motivated us to create even better learning content and courses experience for you . Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
We are super happy that Edureka is helping you learn better. Your support means a lot to us and it motivated us to create even better learning content and courses experience for you . Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
Awesome explanation . Thank you edureka. Explanation is Very understandable manner to even poor mathematical background people. I need clarification. When R(square) is very less how to increase R(square) to make best fit of line. Is there any formula or mathematical procedure. Kindly clarify me.
Hi Ravi, thanks for the compliment. Try removing any insignificant variables. Usually when there are more predictor variables in the data set, the R square value tends to decrease.
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Need help what if the coordinates are not continuous on the x axis for example x-axis = 0 1 2 3 4 5 y-axis = 0 1 2 3 4 5 coordinates = (2,3) (4,3) there is a gap between the x coordinates so is it possible to get the regression line ?
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Very Good , looking for Mathematical understanding for prediction and code implementation. Very Much satisfied to understand prediction derivation. Thanks much.
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Awesome video...made a complex understanding to a very simple understanding..trust me I was struggling to understand the Liner Regression for more then 1 month and my struggle ends just in 38 min of this video.. the explanation was superb Thank you is a little word.. God Bless you..
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You're welcome 😊 Stay connected with our channel and team :) . Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
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I was revising the topics and getting prepared for the Job interview, and going through this set of videos. I really like the way the topic is explained thoroughly with examples and animation. It covered most of the parts related to this topic. I really like the presentation, would suggest whoever planning to understand the topics related to this.
We are very glad to hear that your a learning well with our contents :) continue to learn with us and don't forget to subscribe our channel so that you don't miss any updates !
Hi Ishwar, thanks for appreciating our work! Please share your email id with us (it will not be published). We will forward you the dataset to your email address.
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Hi Vikas, You can check out our Machine Learning playlist. We have explained Machine Learning with the help of real world example and projects. ruclips.net/video/Pj0neYUp9Tc/видео.html
Hi there. This really cannot be fully predicted. It is based on the application that you use. You can try all three models and then check the accuracy and accordingly use the required model. Hope that helps your query.
According to the formula for r-square, there is no subtraction from one... but in the implementation via coding, i noticed you subtracted from 1. why is that?
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Is there any acceptable range for R^2??? As for the inclusion and exclusion criteria of a dependent variable the p-value of linear regression has a range..
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Hi Jaisha, R-squared is a statistical measure that represents the extent to which the predictor variables (X) explain the variation of the response variable (Y). For example, if R-square is 0.7, this shows that 70% of the variation in the response variable is explained by the predictor variables. Therefore, the higher the R squared, the more significant is the predictor variable. Hope this is helpful!
When more variables are added, r-squared values typically increase. They can never decrease when adding a variable; and if the fit is not 100% perfect, then adding a variable that represents random data will increase the r-squared value with probability. Hope that solves your query.
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Where is the dataset?
For Stock Price predictor can day be Independent variable and Price be Dependent variable. Is Linear regression fit for Stock Market Price predictors??
The main difference between them is that the output variable in regression is numerical (or continuous) while that for classification is categorical (or discrete). However, they both are categorized under the same umbrella of supervised machine learning.
If the goal is prediction or forecasting or error reduction, linear regression can be used to fit a predictive model to an observed data set of y and x values.
The explanation was top-notch, Kudos to the instructor and specially Edureka for making this, Thank You.
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24:20 - Programming part
You are a world-class instructor, your speaking skills are really good. Even I had completed the video in 2x speed, I did not find any place where I have to replay the part. The linear regression algorithm is explained in the easiest possible way. Thanks for the effort!!!!
You guys have a video on literallyy every topic, don't ya🔥❤️😂
the perfect explanation of linear regersssion model
Thank You 😊 Glad it was helpful!!! Keep learning with us..
Thanks for creating this video, was looking for a simple explanation, had wasted 3-4 days finding good video, finally got this, thanks again. Was trying to follow along on excel sheet, I wish graphs were plotted in google sheet or excel.
best video i have ever seen on linear regression
Hey:) Thank you so much for your sweet words :) Really means a lot ! Glad to know that our content/courses is making you learn better :) Our team is striving hard to give the best content. Keep learning with us -Team Edureka :) Don't forget to like the video and share it with maximum people:) Do subscribe the channel:)
watched lots of videos to learn this. understood nothing. but, u guys just killed it.
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A great video with all explanations about the model 👍 Could you please explain why one was subtracted while calculating r squared value
The only video that helped me in calculating the best fit line in so much detail and plain english. Thanks for the same.
really a great work.kindly explain the code slowly
thank you so much for making my life easy............ i am M.Tech(AI) student struggling with linear regression from yesterday
This explanation was so clear, even a man who dont know about machine learning , the person can easily understand the concept.
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Excellent lecture.I've followed many lectures, in linear regression analysis, but those were useless,but, your lecture was good,
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Awesome explanation . Thank you edureka. Explanation is Very understandable manner to even poor mathematical background people. I need clarification. When R(square) is very less how to increase R(square) to make best fit of line. Is there any formula or mathematical procedure. Kindly clarify me.
Hi Ravi, thanks for the compliment. Try removing any insignificant variables. Usually when there are more predictor variables in the data set, the R square value tends to decrease.
the best explaination i have ever seen on linear regression. simple and the best way to understand. thank u so much
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Excellent Session. Could you please share the data set used in this practice.
Now my concept of Liner Regression become clear. Thank you so much for providing free education.
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Can you guys do videos related to quantum computing
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The presentation is great. May I know how you created these presentations (is it power point presentation or something else?)
Hey Amritha, we use power point to create all our presentations. We are glad that you liked it. Cheers :)
The best Linear Regression tutorial i have ever seen!! Thank you edureka!!!
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Need help
what if the coordinates are not continuous on the x axis
for example
x-axis = 0 1 2 3 4 5
y-axis = 0 1 2 3 4 5
coordinates = (2,3) (4,3)
there is a gap between the x coordinates
so is it possible to get the regression line ?
Awesome, It's a great explanation.
I got it, thanks a lot.
Quality Unmatched, A Vigorous teacher.
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can we find glucose value in mg/dl by using sensor value and the standard value of glucose using linear regression??
Can you please explain how the formulae for slope m and R square were derived and their interpretation?
Very Good , looking for Mathematical understanding for prediction and code implementation. Very Much satisfied to understand prediction derivation. Thanks much.
The Best Explanation Ever Seen
You can see The Quality of Teaching
Thank You Edureka!!!!!!!
Thank you so much
I have been seeing various videos to understand this topic but you killed it. I loved the explanation 😍
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This was the best one amongst all the videos I went through.. nice.. I need data set too...
Thanks for the compliment!
Can you please share your email id with us (it will not be published). We will forward you the source code to your email address.
Awesome video...made a complex understanding to a very simple understanding..trust me I was struggling to understand the Liner Regression for more then 1 month and my struggle ends just in 38 min of this video.. the explanation was superb Thank you is a little word.. God Bless you..
Best explaination I have ever seen for linear regression the visual explaination, mathematical theory awesome video please keep posting.
best ever explaination i have watched and you saved much of my time thank you so much
Hey:) Thank you so much for your sweet words :) Really means a lot ! Glad to know that our content/courses is making you learn better :) Our team is striving hard to give the best content. Keep learning with us -Team Edureka :) Don't forget to like the video and share it with maximum people:) Do subscribe the channel:)
The best explanation i have seen on this topic. Thank you.
Wow, thanks!
Helped me in understanding my regular AI lectures
Best explanation on Linear Regression. Only python part is little quick.
Can you provide the data set that you use so that we can practice.
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That last twist of scikit learn saved me from that for loop... thank you so much for this presentation...
Very clear, neat and fantastic explanation to the linear regression.. Very well done!!... Thank you...
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Thank you very much Sir! This is very helping me. May God bless the team!
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29 mints just clear my one week confusions
Superb explanation ☺️ thanks to instructor who explained in very easy way
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Hello team,
Should we need to convert all continuous independent variables to categorical variables for logistic regression?
How can I form the equation if I have 4 variables affecting prediction?
WOW Greetings from Egypt ,wished ur my professor in the college
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What a simple explanation. Kudos, man.🥰🥰🥰
Glad you liked it!
I got it. thanks. great jobs edureka.
I love your tutorial.
Excellent sir!
In first attempt I learnt Linear Regression.
Thank you very much.
please Edurake next video insert the timeline content key note, so that in the video one can navigate to specific key note.. thank you
Thank you so much!!! Damn clear about Linear regression
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Simply Wounderful
Thank You 😊 Glad it was helpful!
Awesome and Qnique way to teach using Mathematical Analytics and Graphs.....Thanks
Great session. Will be greatfull if data set could b shared
Hey Farheen, glad you loved the video. Please do mention your email id and we will send the files to you. Cheers!
Simple and elegant, excellent video for beginner... thank you so much
Thank you for explaining it out in easiest way. Where can I get the csv file please?
Good to know your learning with Edureka :) please share your mail id to share the data sheet! We'll Update you soon !
Beautifully explained, Abridged the gap between Theoretical and practical knowledge. This is what we want!!!!
I was revising the topics and getting prepared for the Job interview, and going through this set of videos. I really like the way the topic is explained thoroughly with examples and animation. It covered most of the parts related to this topic. I really like the presentation, would suggest whoever planning to understand the topics related to this.
We are very glad to hear that your a learning well with our contents :) continue to learn with us and don't forget to subscribe our channel so that you don't miss any updates !
Awesome explanation. Thax for the sharing video. Can you please share the dataset files so that we all can practice. Thanks in Advance.
Hi Ishwar, thanks for appreciating our work! Please share your email id with us (it will not be published). We will forward you the dataset to your email address.
Loved the tutorial and always follow edureka for simple understanding
Glad it was helpful!
finally this video help me
continue.....
Awesome Video!... Very nicely explained and easy to understand.
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Amazing video....really cleared out a lot of confusions. Will it be possible to get the data set?..
Thanks for the compliment! Please mention your email id (it will not be published). We will forward the dataset to your email address.
Very nice and detailed explanation about regression analysis.Thank you so much for edureka for providing this importnat vedio.
Hey:) Thank you so much for your sweet words :) Really means a lot ! Glad to know that our content/courses is making you learn better :) Our team is striving hard to give the best content. Keep learning with us -Team Edureka :) Don't forget to like the video and share it with maximum people:) Do subscribe the channel:)
One of the best videos on you tube
Thank you 😊 Glad it was helpful!
thank you, this video helped me lot in this pandemic, with online classes and offline exams. thank you so much
My Question is there, I have seen lots of videos on youtube but i didn't get way how to use machince learning in real life..
Hi Vikas, You can check out our Machine Learning playlist.
We have explained Machine Learning with the help of real world example and projects.
ruclips.net/video/Pj0neYUp9Tc/видео.html
Wow, this video was beautifully executed. Everything was so well explained.
Sir your voice is so clear thanks to explain
How we know which is best fit algo in regression i.e. Mean sqaure error, measure by loss or R Squared? Can we use any one of them.
Hi there. This really cannot be fully predicted. It is based on the application that you use. You can try all three models and then check the accuracy and accordingly use the required model. Hope that helps your query.
Please explain! How to optimise the regression line through the coding & gradient method and how to use it for predictions?
Thank you.
According to the formula for r-square, there is no subtraction from one... but in the implementation via coding, i noticed you subtracted from 1. why is that?
It was very nice and easily understandable...thank u
Great stuff!!Please send in the dataset
In coding part while calculating the R square why you are subtracting it from 1
Excellent explanation and so far the best video i have seen.
it will be very helpful if you share the ppts too
Very impressive sir 🙏🙏
Very well explained. Awesome tutorial Bro.
Well explained, but Coding is something new for me.Need to come from basics.
Fabulous explanation! clear precise and straight to the point.
Hey:) Thank you so much for your sweet words :) Really means a lot ! Glad to know that our content/courses is making you learn better :) Our team is striving hard to give the best content. Keep learning with us -Team Edureka :) Don't forget to like the video and share it with maximum people:) Do subscribe the channel:)
awesome tutorials u guys must come and teach in our college
Excellent video. Would it be possible to share the data set and the code. Many thanks.
Thanks! Please mention your email id (it will not be published). We will forward the code and dataset to your email address.
thank you so much
I have a good understanding of regression now
Is there any acceptable range for R^2???
As for the inclusion and exclusion criteria of a dependent variable the p-value of linear regression has a range..
Nice video .. cleared the concept but extend coding part explanation in detail
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Very Nice explaination. Thanks a lot.
nice session. Can you please send the dataset used for programming?
Thank you. Please share your email id with us (it will not be published). We will forward the dataset to your email address.
it was a nice session. Can you please share code and dataset?
Thank you for the tutorial my friend, greetings from Bolivia
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How the value of R- square can be used to predict the future values for a given input?
Hi Jaisha, R-squared is a statistical measure that represents the extent to which the predictor variables (X) explain the variation of the response variable (Y). For example, if R-square is 0.7, this shows that 70% of the variation in the response variable is explained by the predictor variables. Therefore, the higher the R squared, the more significant is the predictor variable. Hope this is helpful!
Finnest videos on machine learning
R_squared = 1 - SSE(line)/SSE(mean), So, at video 22.26, it should be 70%
maaan thanks alllot your voice and you content was awesome much power to you
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I have question about how to increase R square value for improvements in model.
When more variables are added, r-squared values typically increase. They can never decrease when adding a variable; and if the fit is not 100% perfect, then adding a variable that represents random data will increase the r-squared value with probability. Hope that solves your query.
Nice , well explained.
how "c" value is taken?
Thanks from south Korea
You are welcome👍
really good explanation of coding part .
Thank You I'm very well understand it
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great video! but please improve the sound quality :) thank you very much
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Awesome 😊.... basically a spoon feeding explanation...loved it.