Linear Regression Analysis | Linear Regression in Python | Machine Learning Algorithms | Simplilearn

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This Linear Regression Analysis video will help you understand the basics of linear regression algorithm. You will learn how Simple Linear Regression works with solved examples, look at the applications of Linear Regression and Multiple Linear Regression model. In the end, we will implement a use case on profit estimation of companies using Linear Regression in Python.
Dataset Link - drive.google.com/drive/folder...
Below topics are covered in this Linear Regression Analysis Tutorial:
1. Introduction to Machine Learning
2. Machine Learning Algorithms
3. Applications of Linear Regression
4. Understanding Linear Regression
5. Multiple Linear Regression
6. Usecase - Profit estimation of companies
What is Linear Regression Analysis?
Machine Learning is an application of Artificial Intelligence (AI) that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed. Linear regression is a statistical model used to predict the relationship between independent and dependent variables by examining two factors:
Which variables, in particular, are significant predictors of the outcome variable?
How significant is the regression line in terms of making predictions with the highest possible accuracy?
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Опубликовано:

25 мар 2018

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Комментарии : 1,6 тыс.

@SimplilearnOfficial Год назад

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@SimplilearnOfficial 6 лет назад

Machine Learning is the Future and yours can begin today. Comment below with your email to get our latest Machine Learning Career Guide. Let your journey begin. Do you have any questions on this topic? Please share your feedback in the comment section below and we'll have our experts answer it for you. Also, if you would like to have the dataset for implementing Linear Regression in Python, please comment below and we will get back to you. Thanks for watching the video. Cheers!

@joshuabarnes1031 6 лет назад

I would like to have access to the data set! It would be awesome if it were pubically available on Github or something like that. Awesome video!

@KukaKaz 4 года назад

could you please send the dataset to tolekbaeva@bk.ru. Id really appreciate it!!

@parisingh3860 4 года назад

Could you please send the dataset to parisingh541@gmail.com

@sharadmathur5665 4 года назад

Very informative tutorial ! Please send me the dataset csv file on id sharad.mathur2@gmail.com for hands-on. it would be quite helpful

@sameerkumar-ei6kf 4 года назад

Could you send at sameerkumar21635@gmail.com

@sherenemukherjee7976 3 года назад

Best explanation for regression so far! Most training videos only focus on the code part, which leaves people thinking about the mathematical deductions behind regression. But this covered it all.

@SimplilearnOfficial 3 года назад

WooHoo! We are so happy you love our video. Please do keep checking back in. We put up new videos every week on all your favorite topics. Whenever you have the time, you must also check out our blog page @simplilearn.com and tell us what you think. Have a good day!

@maitreyverma2996 4 года назад

Use "annot" = True parameter in the sns.heatmap() to show the numerical values as well. Makes it much comprehensible.

@go1chase1the1sun1set 4 года назад

When your uni teacher says to google things to answer your questions instead of teaching you but this video has my back haha thankyou!

@SimplilearnOfficial 4 года назад

Happy to help!

@AryaInk 6 лет назад

How can I like this more then once ? The easiest explanation without wasting any time.

@SimplilearnOfficial 6 лет назад

Hi Prashant, we are so happy you love our videos. We are glad to have helped. Do check out our other tutorial videos and subscribe to us to stay connected. Cheers :)

@user-iu1jl9ib2m Год назад

No way to express my gratitude. Amazing explanation with code. I don't how I missed this video for long time

@johnanih56 4 года назад

I wish I could like this more than once. The best so far! Excellent job!

@SimplilearnOfficial 4 года назад

Wow, thanks! Do show your support by subscribing to our channel. Cheers!

@alonhoresh520 6 лет назад

Great video BUT do mind the mistakes: % is percent (not ampersand) and panda.dataframe.corr() is for correlation, not coordinates, In the multiple regression slides, you show ONE variable 'c' and you call it coefficient but sklearn coef. are the slopes, you demonstrate it when you print regressor.coef_ (call 'c' in the slides constant and not coefficient). you do such a good job explaining,these little things ruin some of the fun.

@SimplilearnOfficial 6 лет назад

Noted! We will be careful in our upcoming video tutorials.

@rucker69 5 лет назад

Take your upvote. Surprised they didn't add these corrections to the description.

@SimplilearnOfficial 5 лет назад

We are sorry for the inconvenience! We will definitely raise this issue to the tech team. Thanks.

@robertsharp5422 4 года назад

@@SimplilearnOfficial 'c' in the slides should be called the offset or intercept.

@maxmacken8859 2 года назад

@@rucker69 don't be so rude. they are providing free education. Were you not thought any manners?

@mattpurcell2462 4 года назад

Great video, but when I replicate this for a class it is using an older version of sklearn and says categorical_features has been deprecated. I'm attempting to use ColumnTransformer with mixed results with the provided data. Can you point me in the right direction?

@bellissimo2930 4 года назад

Wonderful detail oriented video . I have one doubt regarding prediction of the profit(new y) for an amount of R&D spend(which will be your new X) which is not present in your current data set. Once you have trained your model with existing data set how will you use it to predict the value of new dependent variable? can you please share the code for that?

@andersonrojas8950 4 года назад

the complexity of the subject matter becomes easy and simple to learn! thank you very much

@SimplilearnOfficial 4 года назад

We are glad you found our video helpful, Anderson. Like and share our video with your peers and also do not forget to subscribe to our channel for not missing video updates. We will be coming up with more such videos. Cheers!

@ariannarisya 7 месяцев назад

Hello. When I did sns.heatmap(companies.corr()), I have error: ValueError: could not convert string to float: 'New York'. I followed all the steps. Thanks

@asimuddin9873 Год назад

Hello! This video is very helpful to understand the basics of Linear Regression but can you update the code where you import sklearn.preprocessing to transform the State column? Since the latest sklearn library removed categorical_features and hence we are getting errors as "TypeError: __init__() got an unexpected keyword argument 'categorical_features", Thank you!

@SimplilearnOfficial Год назад

Hi sorry we can't update the code in this video, but we make sure we will be using the updated library for categorical features in upcoming tutorials. Thank You

@Anwin555 4 года назад

Great video! 🤗 I'm super glad that anyone who wishes to learn whatever tech or skill, they've the opportunity to learn from this amazing community! Many Thanks, #Simplilearn!❤

@SimplilearnOfficial 4 года назад

Hi, thank you for appreciating our work. We are glad to have helped. Do check out our other tutorial videos and subscribe to us to stay connected. Cheers :)

@AbhishekJana11001 5 лет назад

That's what I needed. Thank you so much for the explanation.

@SimplilearnOfficial 5 лет назад

"Hey Mark, we're glad you enjoyed this video! If you want to learn more, you can check out this playlist: ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-ukzFI9rgwfU.html. And don't forget to like, share and subscribe to our channel! :)"

@urbantech28 4 года назад

19:23 I think that's column not row..!

@SimplilearnOfficial 4 года назад

Good eye! Our mistake :

@danieldesantiago9902 5 лет назад

I don't understand why you did not just put the dataset in the description box.

@SimplilearnOfficial 4 года назад

Thanks for your kind comment. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.

@kassywhite9006 4 года назад

Please send it to. Olamidebolarinwa450@gmail.com

@mousumibiswas5791 4 года назад

@@SimplilearnOfficial My email id is mousumi.cse05@gmail.com..please,could you send me this dataset?

@SimplilearnOfficial 4 года назад

Hi, thanks for watching our video. We have sent the requested dataset to your mail ID. Do show your love by subscribing to our channel using this link: ru-vid.com and don't forget to hit the like button as well. Cheers!

@nuel99fc11 4 года назад

@@SimplilearnOfficial mine is nuel.emeruwa@gmail.com

@geethachikkanna8537 3 года назад

This was really helpful and informative. Explained with simple examples

@SimplilearnOfficial 3 года назад

Glad it was helpful!

@justelmij2987 3 года назад

that was the best explanation I had so far to understand Linear regression, thank you sir.

@SimplilearnOfficial 3 года назад

Glad it helped! Thanks for watching!

@nineteen5899 3 года назад

Great video , wish I could meet the teacher and say thank you in person... Truly-knowledge is free if you wanna simply learn....

@SimplilearnOfficial 3 года назад

WooHoo! We are so happy you love our videos. Please do keep checking back in. We put up new videos every week on all your favorite topics. Whenever you have the time, you must also check out our blog page @simplilearn.com and tell us what you think. Have a good day!

@olawaleonafeso1597 2 года назад

Loved this so much. Understood the contents perfectly. Thank you

@SimplilearnOfficial 2 года назад

Glad it was helpful!

@user-im5nx9oi2v 3 года назад

Very helpful video.!!! Why we do not drop the column state? Why we use it to predict the profits? Thank you!

@jaiprakash210 3 года назад

Excellent!!! The Explanation was crystal clear and easy to understand:)

@SimplilearnOfficial 3 года назад

Glad it was helpful!

@mhmonis 5 лет назад

Simply brilliant !!! Best explanation. Well done.

@SimplilearnOfficial 5 лет назад

Hey Mohamed, we're glad you enjoyed the video! If you want to learn more, you can check this playlist out: ru-vid.com/group/PLEiEAq2VkUULYYgj13YHUWmRePqiu8Ddy Also, don't forget to like, share and subscribe! :)

@gouri9969 5 лет назад

Hi, for avoiding dummy variables, how did you decide to skip the 0th or the first column and take the rest of the columns? And in the video, did you say encoder created two instead of one columns ?

@felipejimenezortega7171 4 года назад

Thanks for doing it for free, I hope I can use it in my practice work

@SimplilearnOfficial 4 года назад

Hey Felipe, thank you for watching our video. We are glad that you liked our video. Do subscribe and stay connected with us. Cheers :)

@beyyyyyyyyyy0880 4 года назад

May I ask a question? How to implementation variable x on python if its continuous number, data sample same as like on minutes 09:00. Thanks before, your answer would be help me so much:)

@zusman88 4 года назад

27:03 What if you have more than 1 feature you are doing hot coding for? (ohe=OneHotEncoder(categorical_features=[0,1,2,3]) How many "dummy" variables would I need to delete then?

@DidaKusAlex 2 года назад

Hi, i have to say Amazing video, I had seen a lot of videos looking for a easy way to learn, but this video is the best, I can get it! I think you should update the code because the part "categorical_features" doesn't work. thanks for shared this information and learn us about ML.

@SimplilearnOfficial 2 года назад

You're very welcome!

@natashasamuel9346 2 года назад

Great class. Keep up the good work. Thank You, Natasha Samuel

@SimplilearnOfficial 2 года назад

Keep learning with us .Stay connected with our channel and team :) . Do subscribe the channel for more updates : )

@rockfighter9974 Год назад

Hi Sir /Ma'am, Why avoided R&D Spend column from model. I think this is the indipendent variable so why not considering in model. Please explain

@sebastianfarias5670 2 года назад

Thank you soooo much for such an amazing video on linear regression, +1 sub !!!!!

@SimplilearnOfficial 2 года назад

Thanks for the sub!

@questforprogramming 5 лет назад

You are amazing... thank you for showing us LR in python sklearn...

@SimplilearnOfficial 5 лет назад

Greetings! Thank you for your kind words. Spread the word by liking, sharing and subscribing to our channel! Cheers :)

@mootasr 4 года назад

Wonderful video, very informational. Thank you for sharing! Kindly provide me with the dataset

@SimplilearnOfficial 4 года назад

Hi, please provide your email id here. We will send you the datasets to your email promptly. Thanks!

@winneronuba8173 2 года назад

I just got to know of simplilearn and good to know it's worth sharing The detailed explanation is spot on Can I have the dataset used please?

@SimplilearnOfficial 2 года назад

Hello, thanks for viewing our tutorial. You can find your requested dataset in the video description. Hope that helps.

@imtiazahmad7826 5 лет назад

fantastic explanation just focused on maths ,i already know the programming part

@SimplilearnOfficial 5 лет назад

Glad you enjoyed our video! We have a ton more videos like this on ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-ukzFI9rgwfU.html. We hope you will join our community!

@ekumamichael3451 4 года назад

please i have issue running the sklearn but i experiences IndexError: too many indices for array

@jaimeandresgarcia7518 8 месяцев назад

Crystal clear. Thanks.

@francomagalong4860 Год назад

Hi! May I know how you got 3 in the equation Y=m*X+c; Y=0.6*3+2.2? Thanks.

@fernandonajera8291 3 года назад

Quick question, RD spend and Marketing spend showed some high correlation, wouldnt you have to drop one of those variables to avoid multicollinearity issues, as in, only look at INDEPENDENT variables? Or does the code take care of that when fitting the data? Thanks for the clarification!

@SimplilearnOfficial 3 года назад

"Hi Fernando, It is not recommended to use those independent variables that have high correlation with the variables. This might lead to high variance and bias. The code does take care of multicollinearity when fitting the data."

@ashish8849 3 года назад

what is the range of r2 score (that is acceptable) to consider our model is best.

@byronexaporriton318 2 года назад

How could I convert more than one categorical variable to numerical? Thanks!!

@nikhilkartha6157 6 лет назад

That best fit line animation from 13:44 to 14:41 is quite an intereseting thing to try and code.

@SimplilearnOfficial 6 лет назад

Hi Nikhil, we appreciate the kind comment. Do support us by joining our community by clicking this link: ru-vid.com. Have a good day!

@vaishalibisht518 5 лет назад

Hey Simplilearn thanks again for this wonderful video. I have seen may 5-6 videos of yours without getting bored. They are very easy to understand and follow. Really appreciate your help. I was planning to do higher studies in Data science but I guess it is not required now :) Can you please share the data set with us. It will be very helpful if its link can be shared in the description box.

@SimplilearnOfficial 5 лет назад

Hello Vaishali, we are glad that you found our video helpful. We also appreciate the kind comment. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.

@vaishalibisht518 5 лет назад

It will be very helpful if you can share your email id.

@SimplilearnOfficial 5 лет назад

Sure. We are sharing the mail ID of our employee. Check this: kennet.rajan@simplilearn.net

@sayankaennakham9811 5 лет назад

This is fantastic and thank you so much. Is it possible to get the dataset file so I can practice more please ? Thanks again for another wonderful clip. Regards, Sayan

@SimplilearnOfficial 5 лет назад

Hello Sayan, thanks for the kind comment. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.

@gajananphutane1876 3 года назад

Nicely explained. Thank you.

@SimplilearnOfficial 3 года назад

Glad it was helpful!

@serranneru8855 5 лет назад

Very good structure of explaining it.

@SimplilearnOfficial 5 лет назад

Hey Serran, thank you for appreciating our work. We are glad to have helped. Do check out our other tutorial videos and subscribe to us to stay connected. Cheers :)

@rayrivera1830 4 года назад

best video on ML. great stuff.

@SimplilearnOfficial 4 года назад

Glad you enjoyed our video! We have a ton more videos like this on our channel. We hope you will join our community!

@VarunKumar-fr6ix 5 лет назад

Great video thank you

@SimplilearnOfficial 5 лет назад

Hey Varun, thank you for appreciating our work. We are glad to have helped. Do check out our other tutorial videos and subscribe to us to stay connected. Cheers :)

@lenindesabi5122 5 лет назад

we want use one hot encoder we can fit the model without it?

@Rkcuddles 3 года назад

I love the simply learn content. Wish you would use public data sets so I don’t have to ask for every single file. The delay represents a barrier to me actually trying to code along :(

@SimplilearnOfficial 3 года назад

Glad you enjoyed our video! We have a ton more videos like this on our channel. We hope you will join our community!

@anoushkapalvia7233 10 месяцев назад

Thank you so much for this video and I would like to get the python code used in this tutorial.

@deepikasingh2084 4 года назад

Hey simplilearn!! Thankyou very much for such an informative and helpful ML series. Learning a lot from you guys..for further learning, please provide me your data set files .. thank-you

@SimplilearnOfficial 4 года назад

Hello Deepika, thanks for viewing our tutorial and we hope it is helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.

@deepikasingh2084 4 года назад

@@SimplilearnOfficial mail-id is deepikasingh153.ns@gmail.com Thankyou

@strugglingprogrammer1487 4 года назад

please share data set with me on mail id aksahuboss@gmail.com

@rankzkate 3 года назад

Amazing video.Thank you

@SimplilearnOfficial 3 года назад

Thank you too!

@generalideamedia1283 Год назад

So my question is when doing predictions, are always given a guide (My lecturer calls it a lab) to follow or we have to come with the formats ourselves. Because I believe different predictions come with different times depending on the CSV contents.

@generalideamedia1283 Год назад

I meant "..comes with different format*"

@asheeshyadav5519 4 года назад

When I calculate r2_score then it shows R^2 score is not well-defined with less than two samples

@aakashsharma817 4 года назад

hi sir when we are using OneHotEncoder it will not working because categorical_features attribute in OneHotEncoder is not available anymore, then Sir please help how can we select column on which we want to apply OneHotEncoder waiting for your reply ???

@anniebora4841 4 года назад

what if the r square is not close to 1 and the value slides away to faraway from 1 how can we go for calculation of r square to close to 1.

@maYYidtS 5 лет назад

well explanation.... you take linear regression(sk learn librery) right it is for simple or multiple regression..! becuse we applaying more than one independent variables.

@maYYidtS 5 лет назад

plz share the dataset to mayyi2609@gmail.com

@SimplilearnOfficial 5 лет назад

Hi Mahesh, thanks for watching our video. We have sent the requested dataset to your mail ID. Coming to your query, it is a multiple linear regression because we are dealing with has more than 1 independent variable. Hope that helps!

@vinayreddyvarikuti 5 лет назад

Simplilearn Hi could you please share the Dataset to varikutivinay@gmail.com

@SimplilearnOfficial 5 лет назад

Hi Vinay, thanks for watching our tutorial. We have shared the dataset to your mail ID. Do subscribe, like and share to stay connected with us. Cheers :)

@akshaymestry7339 5 лет назад

thank you for explaining.whats the next step after running the entire code, how to use it to get the result?

@SimplilearnOfficial 5 лет назад

The code for this demo has all that you need to build a linear regression model, predict the values, and measure the accuracy of the model.

@RealtorMasood 5 лет назад

Great Video Thanks for sharing. I have tried it and getting error, NameError: name 'X' is not defined at # Encoding categorical data

@SimplilearnOfficial 5 лет назад

Hi Masood, thanks for watching our video. Please make sure you have used "X" to store the independent variables and imported LabelEncoder and OneHotEncoder from sklearn.prepocessing properly. Do follow the lines of code as shown in the video.

@ritacmilani 5 лет назад

How can I perform a heteroscedasticity test to check the variance of the variables? This is one of the first things to check before running a regression, otherwise all the results will be biased.

@tllittle Год назад

Superb, thanks

@SimplilearnOfficial Год назад

Most welcome 😊

@Rob-jg6vi 5 лет назад

If we had all 50 states that we wanted to use for creating regression, would one hot encoding still be practical? What methods should we use when have over 100 categories with a big data set?

@SimplilearnOfficial 5 лет назад

Even if there are 50 states, one hot encoding will still be handy to convert categorical variables into a form that could be provided to machine learning algorithms to do a better job in prediction.

@anaswahid8520 5 лет назад

In this video residual analysis of simple and multiple regression are not discussed. Please upload the video on residual analysis.

@aleksandrinalikova8101 3 года назад

BIG THANK YOU FOR THE VIDEO. Exactly what I was looking for :) How we calculate the Linear Regression and the Implementation in Python. Amazing! Can I ask you if you can send me the data set as well, please. Thank you in advance!

@SimplilearnOfficial 3 года назад

You are welcome! Please share your email ID to receive the dataset. Thanks.

@darshanD_o7 5 лет назад

Awesome!!!

@SimplilearnOfficial 5 лет назад

Hi Darshan, we appreciate the kind comment! enjoy!

@kevintrang3836 4 года назад

When i run the r2 score code it runs, however I dont get an output?

@aterribleyoutuber9039 3 года назад

Thank you very much!!!!!

@SimplilearnOfficial 3 года назад

You're welcome!

@alpha001ful 6 лет назад

I saw a strong correlation between R&D Spend and Marketing Spend. Don't you think it will cause multi-collinearity ? So we need to eliminate one of the independent variables, either of them. Is there a tutorial how to eliminate or play with independent variables ?

@SimplilearnOfficial 6 лет назад

Hi Sumit, thanks for checking out our tutorial. The data used in this video was generated randomly and not taken from an external source. Yes, there might be a problem of multicollinearity - a scenario where two or more variables are highly correlated and you can predict one variable out of another. To make sure this doesn't lead to wrong results, we have used X=X[:, 1:] for avoiding the dummy variable trap.

@ahmadzaimhilmi 5 лет назад

Try pandas.getdummies()

@raufodilov2203 4 года назад

very helpful video thank you

@SimplilearnOfficial 4 года назад

Glad it was helpful!

@christopheboven6589 3 года назад

Great video, but in newer versions of sklearn the OneHotEncoder categorical_features is deprecated. How would i go about that in the newer version? Thanks.

@SimplilearnOfficial 3 года назад

"Hi Christophe, Please use ColumnTransformer instead. Here is an example: from sklearn.compose import ColumnTransformer ct = ColumnTransformer([(""Name_Of_Your_Step"", OneHotEncoder(),[0])], remainder=""passthrough"")) ct.fit_transform(X) "

@christopheboven6589 3 года назад

@@SimplilearnOfficial Ok thanks, figured it out.

@ekumamichael3451 4 года назад

thanks for the dataset

@SimplilearnOfficial 4 года назад

You are welcome! Do subscribe to our channel for more interesting videos.

@antonioprado9 5 лет назад

can you please tell me what you call the model? is the model the equation you derived at the end? the entire set of data+tools+final result? or something else? thank you ./a/.

@SimplilearnOfficial 5 лет назад

The model is a linear regression model where we are predicting the profit based on various expenditures. After training the model, we are calculating the accuracy of the model.

@husnulaman9197 4 года назад

Hi there, great tutorial. But I've watched many tutorial on linear regression, one thing I don't understand is those 200 predictions; are they future data? I mean if I do it for Time vs Temperature; does that 20% means temperature for next 20 years? Or is there a way I can predict the temperature for next 20 years using coeff value. Please help 😭

@kabrantie 4 года назад

20% is not future data, it's real data in the past, You use that to test the effectiveness of the model.

@fanBladeOne 4 года назад

"Holy Shit". This was literally what I said when it did in fact seem a custom dataframe's all that's necessary for an accurate correlation chart. I still cannot believe it was this easy. Thanks a lot for showing us all!

@SimplilearnOfficial 4 года назад

Hello, thank you for watching our video. We are glad that you liked our video. Do subscribe and stay connected with us. Cheers :)

@sushilsawant8356 5 лет назад

Hi Sir, can you show the visualization on the prediction output

@SimplilearnOfficial 5 лет назад

Please refer to this seaborn library article to visualize any linear regression using Python: seaborn.pydata.org/tutorial/regression.html Here is another prediction example to analyze your dataset: towardsdatascience.com/linear-regression-using-python-ce21aa90ade6

@subhakantasahoo2795 5 лет назад

Nice video. Very well explained. Can I have the dataset used?

@SimplilearnOfficial 5 лет назад

Hello Subhakanta, thanks for appreciating our work. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.

@rukesh07 4 года назад

Excellent explanation

@SimplilearnOfficial 4 года назад

Thanks for the kind comment! Do show your love by subscribing to our channel using this link: ru-vid.com and don't forget to hit the like button as well. Cheers!

@NR-xt8mq 3 года назад

Hi! Thank you so much for the explanation. It's very helpful for me to learn. Anyway, can you please be kind and send me the csv file you used? And once again.. Thank you

@bhavishyagrover2420 5 лет назад

Hi , Can u please share the data set to understand it better.. and the explanation was good

@SimplilearnOfficial 5 лет назад

Hello Ranvi, thanks for viewing our tutorial and we hope it is helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.

@frankesogwa1402 4 года назад

The tutorial was insightful. Please, I'll like to have the datasets for the tutorial. Thank you.

@SimplilearnOfficial 4 года назад

Hello Frank, thanks for viewing our tutorial and we hope it is helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.

@TheAbid1992 3 года назад

@@SimplilearnOfficial can you share the dataset. Here is my mail address: green.abid@gmail.com

@poojapauline7379 6 лет назад

I have an error. instead of using state as 4th column(3rd as count from 0), I used it as 5th column(4th). when I tried to change that string to number through linearencoder and onehotencoder with X[:,4], I am getting error: "index 4 is out of bounds for axis 1 with size 4". please help me.

@SimplilearnOfficial 6 лет назад

Hi Pooja, thanks for checking our tutorial. If you are trying to shift the State column from 3rd to 4th, make sure the data in the 3rd column is not empty. All the 5 columns in the data should be adjacent to each other. Any break in the data will show an error. Otherwise y=companies.iloc[:, 4].values won't take profit as the target column. You might have to change it to y=companies.iloc[:, 5]. Hope that helps!

@cheerucheeru4574 6 лет назад

Really liked the presentation of the video. Great. How can i get access to the dataset in the video to work. Could you please let me know. Thanks.

@SimplilearnOfficial 6 лет назад

Hi, thanks for watching our video and appreciating our work. It would be great if you could share your email ID so that we can send the dataset immediately If you want your email ID to be kept hidden, we can do that as well. Thanks.

@cheerucheeru4574 6 лет назад

Simplilearn hi my id is cheerusai@gmail.com. .thanks for your follow up. .

@SimplilearnOfficial 5 лет назад

Hi Cheeru, thanks for checking out our tutorial. We have sent the requested dataset to your mail ID. Also, Subscribe to our channel by clicking on the bell icon for never missing another update. Cheers!

@udhayakumarsathiyanarayana5312 6 лет назад

Hi.. Great video of linear regression with proper example. Please can you share dataset..??

@udhayakumarsathiyanarayana5312 6 лет назад

My mail ID: uk2804@gmail.com

@SimplilearnOfficial 6 лет назад

Hi Udhay, we are glad that you liked our video. We have sent the requested dataset to your mail ID. Do check out our other tutorial videos and subscribe to us to stay connected. Cheers :)

@imaadullahafridi1928 4 года назад

Thank you very much for such a simple explanation, got a lot to learn, Can I please get the data and the code please Thanks Imaad Ullah

@SimplilearnOfficial 4 года назад

Hi Imaad, thanks for watching our video and appreciating our work. We have sent the requested dataset to your mail ID. Do show your love by subscribing our channel using this link: ru-vid.com and don't forget to hit the like button as well. Cheers!

@MS-qn9oo 5 лет назад

It would be great if you would have interpreted the results of the Linear Regression test.. I don't understand what it mean the result of prediction ? what and how it will be going to benefit to take the decision

@SimplilearnOfficial 5 лет назад

The result of the model is to predict the profit based on the expenditure the company makes. The test set contains 200 rows of data for which we are calculating the profit (y_pred), based on the model we trained using the training data of 800 rows (X_train).

@vigilhammer 5 лет назад

Beautiful and awesome

@SimplilearnOfficial 5 лет назад

Hey, thank you for watching our video. We are glad that you liked our video. Do subscribe and stay connected with us. Cheers :)

@ashutoshse1 4 года назад

Hey your onehotencoder does not work, how can i use it for categorical data?

@aimharahap9744 5 лет назад

very nice explanation. could you please share the dataset in this video pls. thanks

@SimplilearnOfficial 5 лет назад

Hey Harahap, thank you for appreciating our work. We are glad to have helped. Do check out our other tutorial videos and subscribe to us to stay connected. Cheers :)

@merhaiakshay9625 2 года назад

is it possible to get the powerpoint presentation? please

@bharathjc4700 6 лет назад

great video pls share the notebook and dataset

@SimplilearnOfficial 6 лет назад

Hi Bharath, we have shared the requested dataset and code source as well. Spread the word by liking, sharing and subscribing to our channel! Cheers :)

@karthikeyanmani5144 5 лет назад

Using formula we are getting m slope and c intercept ,which is the best fit line ,then why we need to rotate the line in data point to get the best fit line ?

@SimplilearnOfficial 5 лет назад

It's a general idea in Linear Regression that we keep shifting the regression line to make sure the sum of squared distance is least from all the data points to the line. Hope that helps!

@aarishmahmood8865 5 лет назад

Simplilearn Won't shifting the regression line changes the intercept?

@SimplilearnOfficial 5 лет назад

Shifting the regression line will certainly change the slope and the intercept of the line. In Linear Regression, we always consider the slope and intercept of the best fit line whose sum of squared distance is least from all the data points. Hope that helps!

@gustavoandres8093 5 лет назад

hi, can you explain me how "AVOIDING DUMMY VARIABLE TRAP, X=X[:,1:]" works ? i think onehotencoded works like pivot, so its create a column for every diferent value. numpy array dont create a name for every column as pandas do, so i think when you delete the first column you actualy delete data.

@SimplilearnOfficial 5 лет назад

Dummy variable takes discrete values such as 1 or 0 to mark the presence and absence of a particular category. This scenario arises when two or more variables are highly correlated. You need to avoid such variables as it would divert the output results. To learn more on this please refer to analyticstraining.com/understanding-dummy-variable-traps-regression/

@muhammadsayed4620 4 года назад

Excellent 👌

@SimplilearnOfficial 4 года назад

Hello, thank you for watching our video. We are glad that you liked our video. Do subscribe and stay connected with us. Cheers :)

@jongcheulkim7284 3 года назад

Thank you for the lecture. It was very helpful. May I get a copy of the CVS file? Thank you.

@SimplilearnOfficial 3 года назад

Hello, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we can send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that too. Hope that helps.

@anongeorge2504 4 года назад

Great video

@SimplilearnOfficial 4 года назад

Glad you enjoyed it!

@AmonMRBAZONGO 6 лет назад

Hi ... great resource. Is it possible to have the dataset ? thanks and keep it up

@SimplilearnOfficial 6 лет назад

Sure Amon, could you share your email id with us? We can email you the dataset used in this video.

@SimplilearnOfficial 6 лет назад

Amon, you can share your email and we would not make your comment public if in case that is a concern. The email would be used only for sharing the dataset you asked for. Cheers!

@raffaeltwinyoshua899 6 лет назад

Do you have the tutorial for Deep Learning (ANN,CNN, RNN, LSTM )? thanksss

@SimplilearnOfficial 6 лет назад

Hi Raffael, we have a couple of videos which cover the topics which you have mentioned. Check these videos out: 1. ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-lWkFhVq9-nc.html 2. ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-Jy9-aGMB_TE.html Do subscribe, like and share to stay connected with us. Cheers :)