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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[@go1chase1the1sun1set]

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]

Happy to help!

[@maitreyverma2996]

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

[@alonhoresh520]

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]

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

[@rucker69]

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

[@SimplilearnOfficial]

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

[@robertsharp5422]

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

[@maxmacken8859]

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

[@AryaInk]

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

[@SimplilearnOfficial]

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 :)

[@sherenemukherjee7976]

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]

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!

[@mattpurcell2462]

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?

[@user-iu1jl9ib2m]

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

[@bellissimo2930]

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?

[@johnanih56]

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

[@SimplilearnOfficial]

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

[@asimuddin9873]

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]

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

[@andersonrojas8950]

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

[@SimplilearnOfficial]

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]

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

[@urbantech28]

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

[@SimplilearnOfficial]

Good eye! Our mistake :

[@SimplilearnOfficial]

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]

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]

could you please send the dataset to [e-mail]. Id really appreciate it!!

[@parisingh3860]

Could you please send the dataset to [e-mail]

[@sharadmathur5665]

Very informative tutorial ! Please send me the dataset csv file on id [e-mail] for hands-on. it would be quite helpful

[@sameerkumar-ei6kf]

Could you send at [e-mail]

[@gouri9969]

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 ?

[@user-im5nx9oi2v]

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

[@danieldesantiago9902]

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

[@SimplilearnOfficial]

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]

Please send it to. [e-mail]

[@mousumibiswas5791]

@@SimplilearnOfficial My email id is [e-mail]..please,could you send me this dataset?

[@SimplilearnOfficial]

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: kzbin.info and don't forget to hit the like button as well. Cheers!

[@nuel99fc11]

@@SimplilearnOfficial mine is [e-mail]

[@AbhishekJana11001]

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

[@SimplilearnOfficial]

"Hey Mark, we're glad you enjoyed this video! If you want to learn more, you can check out this playlist: kzbin.info/www/bejne/q5zdd3xvp8yqnLc. And don't forget to like, share and subscribe to our channel! :)"

[@zusman88]

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?

[@nineteen5899]

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

[@SimplilearnOfficial]

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!

[@Anwin555]

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]

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 :)

[@mhmonis]

Simply brilliant !!! Best explanation. Well done.

[@SimplilearnOfficial]

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

[@geethachikkanna8537]

This was really helpful and informative. Explained with simple examples

[@SimplilearnOfficial]

Glad it was helpful!

[@olawaleonafeso1597]

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

[@SimplilearnOfficial]

Glad it was helpful!

[@beyyyyyyyyyy0880]

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:)

[@justelmij2987]

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

[@SimplilearnOfficial]

Glad it helped! Thanks for watching!

[@felipejimenezortega7171]

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

[@SimplilearnOfficial]

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

[@jaiprakash210]

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

[@SimplilearnOfficial]

Glad it was helpful!

[@rockfighter9974]

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

[@jaimeandresgarcia7518]

Crystal clear. Thanks.

[@bharathbhimshetty8926]

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

[@SimplilearnOfficial]

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

[@francomagalong4860]

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

[@DidaKusAlex]

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]

You're very welcome!

[@fernandonajera8291]

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]

"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."

[@ekumamichael3451]

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

[@nikhilkartha6157]

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

[@SimplilearnOfficial]

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

[@imtiazahmad7826]

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

[@SimplilearnOfficial]

Glad you enjoyed our video! We have a ton more videos like this on kzbin.info/www/bejne/q5zdd3xvp8yqnLc. We hope you will join our community!

[@sayankaennakham9811]

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]

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.

[@byronexaporriton318]

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

[@mootasr]

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

[@SimplilearnOfficial]

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

[@ashish8849]

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

[@vaishalibisht518]

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]

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]

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

[@SimplilearnOfficial]

Sure. We are sharing the mail ID of our employee. Check this: [e-mail]

[@lenindesabi5122]

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

[@maYYidtS]

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]

plz share the dataset to [e-mail]

[@SimplilearnOfficial]

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]

Simplilearn Hi could you please share the Dataset to [e-mail]

[@SimplilearnOfficial]

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 :)

[@generalideamedia1283]

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]

I meant "..comes with different format*"

[@VarunKumar-fr6ix]

Great video thank you

[@SimplilearnOfficial]

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 :)

[@winneronuba8173]

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]

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

[@natashasamuel9346]

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

[@SimplilearnOfficial]

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

[@serranneru8855]

Very good structure of explaining it.

[@SimplilearnOfficial]

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 :)

[@jenypowell6174]

I have tried updating the code from OneHotEncoder to Column_Transformer and I'm having touble getting it to work... ct = ColumnTransformer([("State", OneHotEncoder(), [3],)], remainder = "passthrough") ct.fit_transform(X) returns an error "Expected 2D array, got scalar array instead" What is the correct way to write this code now that OneHotEncoder is deprecated? Thanks for any input!

[@Rkcuddles]

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]

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

[@sebastianfarias5670]

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

[@SimplilearnOfficial]

Thanks for the sub!

[@christopheboven6589]

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]

"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]

@@SimplilearnOfficial Ok thanks, figured it out.

[@gajananphutane1876]

Nicely explained. Thank you.

[@SimplilearnOfficial]

Glad it was helpful!

[@anaswahid8520]

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

[@akshaymestry7339]

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

[@SimplilearnOfficial]

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.

[@tllittle]

Superb, thanks

[@SimplilearnOfficial]

Most welcome 😊

[@alpha001ful]

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]

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]

Try pandas.getdummies()

[@rayrivera1830]

best video on ML. great stuff.

[@SimplilearnOfficial]

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

[@RealtorMasood]

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

[@SimplilearnOfficial]

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.

[@rankzkate]

Amazing video.Thank you

[@SimplilearnOfficial]

Thank you too!

[@deepikasingh2084]

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]

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]

@@SimplilearnOfficial mail-id is [e-mail] Thankyou

[@strugglingprogrammer1487]

please share data set with me on mail id [e-mail]

[@anoushkapalvia7233]

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

[@frankesogwa1402]

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

[@SimplilearnOfficial]

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]

@@SimplilearnOfficial can you share the dataset. Here is my mail address: [e-mail]

[@fanBladeOne]

"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]

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

[@udhayakumarsathiyanarayana5312]

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

[@udhayakumarsathiyanarayana5312]

My mail ID: [e-mail]

[@SimplilearnOfficial]

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 :)

[@asheeshyadav5519]

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

[@aakashsharma817]

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 ???

[@cheerucheeru4574]

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]

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]

Simplilearn hi my id is [e-mail]. .thanks for your follow up. .

[@SimplilearnOfficial]

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!

[@Rob-jg6vi]

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]

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.

[@sushilsawant8356]

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

[@SimplilearnOfficial]

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]

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

[@SimplilearnOfficial]

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.

[@bhavishyagrover2420]

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

[@SimplilearnOfficial]

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.

[@darshanD_o7]

Awesome!!!

[@SimplilearnOfficial]

Hi Darshan, we appreciate the kind comment! enjoy!

[@kevintrang3836]

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

[@pphot1]

Thank you for the very good explanation. Request you to please share the dataset to self try the explained code.

[@SimplilearnOfficial]

Hi Prabir, thanks for checking out our tutorial. It would be great if you could provide your email Id so that we can send the dataset to your mail ID. Hope that helps!

[@pphot1]

[e-mail]

[@xmohd2011]

​@@pphot1 Can i get the dataset as well from you

[@SimplilearnOfficial]

Hello Mohammed, 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.

[@SimplilearnOfficial]

Hi Prabir, thanks for sharing your email ID. We have sent the requested dataset to your mail ID. Do subscribe to our channel and stay tuned.

[@anniebora4841]

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.

[@husnulaman9197]

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]

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

[@ritacmilani]

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.

[@aterribleyoutuber9039]

Thank you very much!!!!!

[@SimplilearnOfficial]

You're welcome!

[@rukesh07]

Excellent explanation

[@SimplilearnOfficial]

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

[@AmonMRBAZONGO]

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

[@SimplilearnOfficial]

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

[@SimplilearnOfficial]

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!

[@antonioprado9]

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]

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.

[@MS-qn9oo]

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]

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).

[@aimharahap9744]

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

[@SimplilearnOfficial]

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]

is it possible to get the powerpoint presentation? please

[@bharathjc4700]

great video pls share the notebook and dataset

[@SimplilearnOfficial]

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 :)

[@vigilhammer]

Beautiful and awesome

[@SimplilearnOfficial]

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

[@karthikeyanmani5144]

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]

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]

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

[@SimplilearnOfficial]

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!

[@jongcheulkim7284]

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

[@SimplilearnOfficial]

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.

[@gustavoandres8093]

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]

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/

[@imaadullahafridi1928]

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]

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: kzbin.info and don't forget to hit the like button as well. Cheers!

[@raffaeltwinyoshua899]

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

[@SimplilearnOfficial]

Hi Raffael, we have a couple of videos which cover the topics which you have mentioned. Check these videos out: 1. kzbin.info/www/bejne/oojOd5uMpp5gpMU 2. kzbin.info/www/bejne/gKqcXpR9gqeSiqc Do subscribe, like and share to stay connected with us. Cheers :)

[@raufodilov2203]

very helpful video thank you

[@SimplilearnOfficial]

Glad it was helpful!

[@ashutoshse1]

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

[@NR-xt8mq]

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

[@ekumamichael3451]

thanks for the dataset

[@SimplilearnOfficial]

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

[@omarismail4734]

Hi! Thank you very much for this baby-step explanation! At 24:10 I received TypeError: __init__() got an unexpected keyword argument 'categorical_features' I found out that the 'categorical_features' keyword was deprecated in version 0.20 and has been removed in 0.22 Any replacement command code based on your lines and get the same results? Thanks XOXO

[@liboshen4045]

use ColumnTransformer instead, bro

[@daraputharakeswarareddy4347]

@@liboshen4045 column transformer is not working bro. from sklearn.preprocessing import LabelEncoder,OneHotEncoder from sklearn.compose import ColumnTransformer labelencoder=LabelEncoder() X[:,3]=labelencoder.fit_transform(X[:,3]) #onehotencoder= OneHotEncoder() columnTransformer = ColumnTransformer([( OneHotEncoder(), [3])], remainder = 'passthrough') X = np.array(columnTransformer.fit_transform(X).toarray(), dtype = np.float64) X = X[:, 1:]

[@edmundchan8923]

Could you also show how to plot the residual?

[@SimplilearnOfficial]

Please find the following links to plot the residuals in any regression model: www.scikit-yb.org/en/latest/api/regressor/residuals.html docs.statwing.com/interpreting-residual-plots-to-improve-your-regression.