Principal Component Analysis in R: Example with Predictive Model & Biplot Interpretation

[@Drgautham]

Thank you so much Professor🙏

[@bkrai]

You are very welcome!

[@babadrammeh656]

R PCA IS VERY GOOD PACKAGE AND VERY HELPFULL

[@bkrai]

Yes, I agree!

[@abdullahmohammed8521]

Many thanks for you Dr. God bless you.

[@bkrai]

You are most welcome!

[@jinnythomas9815]

Great Explanation....

[@bkrai]

Thanks!

[@ashishsangwan5925]

Awesome Explanation

[@bkrai]

make sure you run following before installing: library(devtools)

[@raisulalam6051]

Thank you

[@bkrai]

Welcome!

[@earlymorningcodes6100]

scatter Plat and Correlation- 2:04

[@bkrai]

Thx

[@souvikmukherjee7977]

sir, please make a session on factor analysis with prediction

[@bkrai]

Thanks for the suggestion!

[@katherinechau5594]

your videos are great :)

[@bkrai]

Thank you!

[@earlymorningcodes6100]

Orthogonality of principal component- 10:17

[@bkrai]

Thx

[@janardhankadari3286]

Interesting

[@bkrai]

thanks!

[@manpreetkaur7716]

Add a video on non negative matrix factorization like intNMF

[@bkrai]

Thanks, I've added it to my list of future videos.

[@shawnmckenzie8699]

To install ggbiplot, the code is now (17, Jan, 2020): library(devtools) install_github("vqv/ggbiplot") source: github.com/vqv/ggbiplot Excellent video and well explained these concepts. Thanks.

[@bkrai]

Thanks for the update!

[@deepikachandrasekaran3554]

Very useful video sir. Could you explain me what is the need to partition the data into training and testing data?

[@bkrai]

You may review this: kzbin.info/www/bejne/l4SUgGt7nqx_msk

[@deepikachandrasekaran3554]

@@bkrai thank you sir.

[@ramram2utube]

I revisited your video for interpretation of biplots in PCA. Many thanks.

[@bkrai]

You are welcome!

[@nyatonkitnya4267]

one really good video i have found. After watching few of your video now your videos are becoming a "turn to" when require. thanks

[@bkrai]

Glad to hear that!

[@earlymorningcodes6100]

partition of data 1:16

[@bkrai]

Thx

[@sonalichakrabarty1618]

Can you please show back propagation algorithm in r

[@bkrai]

Refer to this: kzbin.info/www/bejne/Y4fWaomXmpd-f5I

[@abiani007]

Can you upload a video describing independent component analysis in R

[@bkrai]

I've added it to my list.

[@earlymorningcodes6100]

Bi Plot 11:38

[@bkrai]

Thx

[@bucklasek1]

Thanks for the video! It helped me a lot doing the forecasting for future values using PCA.

[@bkrai]

Very welcome!

[@eldrigeampong8573]

Thank you so much Dr. Rai. Detailed teaching

[@bkrai]

Thanks for comments!

[@WahranRai]

19:12 It is only for purpose to show another way to get the principal component related to training because : identical(pc$x, predict(pc,training)) gives TRUE meaning that pc$x is same as predict(pc,training).

[@bkrai]

That's correct!

[@nyadav378]

Very informative and nice presentation sir, sir can we estimate PCA for factor (for eg species) with unequal no. of observation. And we want to see the correlations in terms of each species viz for setosa or other two, how to do it? Please explain...Thank You

[@srujananeelam6547]

Fantastic session.Perfectly understood Biplot

[@bkrai]

Thanks for comments!

[@donne4real]

Wonderful job explaining the material.

[@bkrai]

Thanks for your comments and finding it useful!

[@Dejia_Space]

Thank you!!Best explanation on Biplot on KZbin .

[@bkrai]

Glad it was helpful!

[@ramram2utube]

Thanks a lot Sir for your nice presentation. You saved my time. Earlier I used your R codes on Kohonen NN and now for PCA for my training lectures. Your explanation is so lucid. I appreciate your noble service of sharing knowledge

[@bkrai]

You are most welcome!

[@affyy04]

Thank you for this amazing video. Better than my university lectures

[@bkrai]

Thanks for comments!

[@jacklu1611]

The Bio-plot was explained very clearly, thank you Dr. Rai!

[@bkrai]

You are welcome!

[@koparka112]

Thank you for the material. It is very clear and actually very relevant to my current work. As I understand, the conversion of the data comprises addition products of notmalized predictors and loadings. Maybe you would have time to post a PLS regression video, please? The intriguing part is the explanation of the model itself

[@deepthibhadran4181]

sir can u please make one video on k means clustering and classification and regression tree analysis

[@bkrai]

See this link: kzbin.info/www/bejne/a5anooWvqMacmdE

[@deepthibhadran4181]

@@bkrai thank you sir

[@bkrai]

You are welcome!

[@deepthibhadran4181]

@@bkrai Sir do you know about WRF model

[@bkrai]

yes

[@dioagusnofrizal9773]

Thanks sir, why in this video use linear regression? Can i use k means to clustering from pc1 and pc2?

[@bkrai]

Which line are you referring to?

[@dioagusnofrizal9773]

Sorry, i mean logistic regression in line 59

[@earlymorningcodes6100]

5:01 Principal component Analysis

[@bkrai]

Thx

[@abhishek894]

Thank you for this nice video Dr. Rai. I have a doubt. Why the predict function was used multiple times. After the prcomp function, all the data of Principle components were available in: pc$x. Why do we have to do: trg

[@bkrai]

In R you can get same thing in multiple ways. This is just for illustration.

[@abhishek894]

@@bkrai Thank you Sir. That makes it clear.

[@bkrai]

@@abhishek894 You are welcome!

[@aks1008]

Sir can I use boruta function instead of pca in r..

[@bkrai]

Yes certainly. Here is the link: kzbin.info/www/bejne/jHalkqtojLKVe6M

[@aks1008]

@@bkrai sir what do you like between r and python..i find r code more easy to understand and write..

[@bkrai]

In universities, business students usually use R and computer science students mostly use Python. If you are mainly looking to apply various machine learning and statistical methodologies, R is perfect.

[@LlamaFina]

Great video! Thanks for sharing your knowledge.

[@bkrai]

Thanks for comments!

[@jinnythomas9815]

Thanks for the video Please publish video on Exploratory Factor Analysis,Confirmatory Factor Analysis application in a model Also please explain the difference from PCA

[@bkrai]

Thanks for the suggestion, I've added this to my list.

[@prithvivasireddy5564]

Awesome video sir...kudos... :) 1 doubt though .... 20:48 - why are we using 2 components only? How do we know how many principal components to use?(species ~ PC1 + PC2)

[@bkrai]

2 PCs capture more than 95% of the variability in the data. Other 2 only add about 5%. So you can choose to have PCs that capture over 80% or 90% of the variability.

[@siddharthabingi]

Great lecture. Thanks.

[@bkrai]

Thanks!

[@inesceciliacardonadevoz5072]

Thanks for this video sir, very good class but I can´t get it. because Error ... could not find function "ggbiplot". Excuse me, which is your R version ?

[@bkrai]

Try this: library(devtools) install_github("vqv/ggbiplot")

[@asiangg]

Thank you. Learned a lot from your channel

[@bkrai]

Thanks!

[@mukhtaradamuabubakar370]

Nice video and very helpful, I have challenges while installing the ggbiplot and mnet packages (am using R version 3.6.3) please any advice on how to over come such challenge?

[@mukhtaradamuabubakar370]

OK for the nnet package it was successfully installed. but still struggling with the ggbiplot (despite using your codes). thanks

[@NIKHILESHMNAIK]

You are too good sir. An absolute treat for ML enthusiasts.

[@bkrai]

Thanks for your comments!

[@soumyanayak445]

Sir why have you predicted the training and test data with respect to PC? can use trg data for making neural model and test using tst data set? and find correlation b/w act and predicted values?

[@bkrai]

When there are many variables, chances of having multicollinearity problem increases. And PCA helps to solve that problem. And yes, you can use neural network model.

[@soumyanayak445]

@@bkrai sir can you please explain me the significance of the lines under the heading: prediction with principle components.As I am unable to understand why we are predicting twice on test data set. Please explain sir

[@bkrai]

To avoid over-fitting where you get very good result from training data but not so from testing.

[@johnstevenson6458]

Great video. Do you have a suggested package for running binary logistic regression? From a brief scan of nnet it appears to only have arguments for multinomial response variables. Thank you.

[@bkrai]

You can refer to this: kzbin.info/www/bejne/d4fbaIqZZqiEbbs

[@johnstevenson6458]

@@bkrai sorry I was unclear in my message. I was hoping for a suggested package to run a binary logistic regression using PCA components as predictors - similar to what you have done here with multinomial. Any suggestions are welcome.

[@bkrai]

Yes, you can use the PCA components as predictors and run binary logistic regression as shown in the link that I sent earlier.

[@MinhasA]

thank you for the amazing video!

[@bkrai]

Thanks for comments!

[@golumworks]

If I just use addEllipses =TRUE, what determines the size of those ellipses? Also, if I specify ellipse.type = “confidence”, what confidence level is used to generate the ellipses? I used factoextra if that helps.

[@ketanverma7839]

is there any other alternative package for ggbiplot ?

[@bkrai]

Try this for biplot ( I just now ran this in RStudio cloud, and it worked fine): library(devtools) install_github("fawda123/ggord") library(ggord)

[@adityapatnaik7078]

too good!! plz make more such videos...plz!

[@bkrai]

Thanks for comments! You may find this useful too: kzbin.info/aero/PL34t5iLfZddu8M0jd7pjSVUjvjBOBdYZ1

[@azzeddinereghais7494]

Good evening If you want to show the first dimension (Dim1) and the third dimension (Dim3) What to do or if you can provide the code for that Thanks

[@Rutvi_patel_1111]

Fabulous work in PCA ! Keep it up

[@bkrai]

Thanks for the feedback!

[@maf4421]

Thank you Dr. Bharatendra Rai for explaining PCA in detail. Can you please explain how to find weights of a variable by PCA for making a composite index? Is it rotation values that are for PC1, PC2, etc.? For example, if I have (I=w1*X+w2*Y+w3*Z) then how to find w1, w2, w3 by PCA.

[@bkrai]

For calculations you can refer to any textbook.

[@statistician2856]

sir my data is showing [ reached getOption("max.print") -- omitted 10 rows ]. the last 10 rows are omitted, how to fix this, please

[@bkrai]

That's just how much gets printed. But all data still remains intact.

[@ainli4125466]

Thank you for sharing, I get an error "Error in plot_label(p = p, data = plot.data, label = label, label.label = label.label, : Unsupported class: prcomp"", when I try to run the ggbiplot. Would you please advise how to fix it?

[@siddharthadas86]

Seriously awesome explanations! Thank you again.

[@bkrai]

Thanks!

[@GeFaaaA]

Hello very nice video!!! i have a question. Do you how i choose how many PC i have to use and which ones ???

[@bkrai]

When you have many PCs, you can select first few that capture almost all variability contained in data.

[@GeFaaaA]

@@bkrai thank you for your response! So I have to test every possible model , right? Do you know if I can use something like a criterion ?

[@bkrai]

It is good to capture over 80% of the variability.

[@anuraratnasiri5516]

Thank you so... much!

[@bkrai]

Thanks for comments!

[@alessandrorosati969]

can a dataset consisting of the principal components and the target variable be used to perform machine learning techniques?

[@bkrai]

Yes, this video shows an example of doing it.

[@ziauddinkhan4189]

Why u partition the data into 80 and 20 % please answer

[@bkrai]

It can be any other ratio too. Eg., 60:40, 70:30, 75:25 or 90:10.

[@ziauddinkhan4189]

@@bkrai my question is that what's the reason behind splitting the data into parts in testing and training either its 8o to 20 or 60 to 40. Thanks

[@bkrai]

To avoid over-fitting where you get very good result from training data but not so from testing.

[@jonimatix]

I really like your explanations in your videos. Keep them coming! Thanks

[@bkrai]

Thanks for the feedback!

[@ariannaschmid3805]

Why do you predict before you build the model? Shouldn't it be the other way around?

[@bkrai]

If you are referring to 18:34 time point, note that the predict function is using principal component 'model'.

[@ariannaschmid3805]

@@bkrai What is it that you are trying to predict there? Compared to what you would predict using the regression model?

[@bkrai]

Using predict function we are generating principal components. Later, we are using these principal components for developing a classification model. This is a small dataset just to illustrate the process. And will be useful for high dimensional data where one deals with 1000s of variables.

[@hr_foods]

Thanks for good video. Sir I am using R 3.6.1 version unable to install devtools and ggbiplot also. If devtools install then show that usethis package is missing please solve my issue.

[@bkrai]

I would suggest upgrade R. Currently it is around 4.

[@hr_foods]

@@bkrai I upgrade it but still this problem happen

[@bkrai]

Try this: library(devtools) install_github("vqv/ggbiplot")

[@hr_foods]

@@bkrai I used these codes but not install error occured

[@bkrai]

After intalling make sure to run library.

[@dejunli6417]

Hi, I want to know from where can I get the iris example data ? thank you!

[@bkrai]

It's inbuilt in R itself. You can access it by running first 3 lines shown in the video.

[@saurabhkhodake]

This video is worth its weight in gold

[@alindonosi4]

Where can I find the raw data of this project?

[@bkrai]

Data used here is available within R.

[@philipabraham5600]

This is the best PCA explanation I have seen anywhere so far. Thank you for sharing your knowledge.

[@bkrai]

Thanks for the feedback!

[@kashgarinn]

Great video, thanks for uploading.

[@bkrai]

Thanks for comments!

[@sathishrs3]

Hi Sir, your materials are simple and wonderful. Pls do one video for xgboost. that would be great.

[@bkrai]

Thanks for the suggestion!

[@sathishrs3]

Bharatendra Rai Thanks a lot sir.

[@flamboyantperson5936]

I agree with sathish ravi, Sir please make a video on xgboost. You are one stop solution for every problem and I will remember you all my life.

[@ramp2011]

Awesome video. Thank you. As time permits can you do a video on use of caret package? thank you

[@bkrai]

Saw this today. Thanks for comments!

[@highway2chill]

thank you a lot for this support sir. If you could provide further guidance it would be very helpful. I am trying to build a models for metastasis prediction using single cell gene expression levels. kindly let me know if it would be possible for you. thanks again

[@bkrai]

You may find this useful: kzbin.info/www/bejne/i5rPY3qQlp2amMk

[@jayashriraghunath3210]

Awesome explanation sir...👍👍can you make a video for independent component analysis using r in the same way sir?

[@bkrai]

Thanks, I've have added it to my list.

[@andreafiore8373]

Thank you, this video will be really helpful to complete my thesis :)

[@bkrai]

Good luck!

[@galk32]

One of the best PCA videos i ever seen, Thank you Mr. Rai.

[@bkrai]

Thanks for comments!

[@theeoddname]

Great Video! Excellent walk though on PCA and how it can be useful for actual classifications. Thanks for the upload.

[@bkrai]

+theeoddname thanks for the feedback!

[@harishnagpal21]

Hi Bharatendra, nice video. I have got couple queries. If there are large no of numeric variables and through PCA we find that they are highly correlated then before going for model building 1) Do we need to remove highly correlated variables ! 2) which one to remove ! Thanks

[@bkrai]

You don't need to remove if you are using the components for developing a prediction model. This video provides a similar example.

[@harishnagpal21]

thanks

[@desert00200]

Principal components are orthogonal to each other, saying differently they are uncorrelated and can be used as is in model building.

[@bkrai]

Thanks!

[@jonm7272]

Thank you for this extremely helpful, and easily understood tutorial, particularly the clear interpretation of the Bi-Plot. Much appreciated

[@bkrai]

You're very welcome!

[@sidraghayas8583]

Can you please help with combined pca and ann model?

[@bkrai]

I'm adding to the list of future videos.

[@PrimoSchnevi]

Hello. I dont know anything about Principal Component Analysis in R: Example with Predictive Model & Biplot Interpretation and i will never need to since thats not in my line of work. I Appreciate your Intromusic though. You are a true champ Bharatendra and enrich this world with your presence. Also that intro music fucking slaps.

[@bkrai]

Thanks for comments!

[@upskillwithchetan]

Really really great explanation sir, Thank you so much for making it very simple

[@bkrai]

Thanks for comments!

[@abhiagni242]

thanks for the video sir... helped a lot :)

[@bkrai]

Thanks for the feedback!

[@naeem3072]

> install_github("ggbiplot","vqv") Error in parse_repo_spec(repo) : Invalid git repo specification: 'ggbiplot' what should i do sir

[@bkrai]

Check if you ran library(devtools)

[@flamboyantperson5936]

This is great. I was looking for PCA and you have done it. Many many thanks to you sir.

[@joujoumilor2898]

As usual your videos are the best explained , Sir please I have a question actually my data contains both numeric and categorical variables, so what is the best method to reduce demension and what is the name of the pckage because I'm new in R .

[@dhavalpatel1843]

You can binaries your categorical variables. Try to google and learn about One-of-k coding. That way you end up with only numerical data.

[@ramp2011]

Another great video. Thank you.. If your data has number of categorical columns, PCA will miss out the dependence of the target variable on these columns. Correct? In such cases what other technique can one use? Thx

[@bkrai]

PCA doesn't include target variable. At the time of developing a predictive model where we use a few principal components, we can include categorical variables. So, we don't miss out on the usefulness of any categorical variable that was excluded from PCA.

[@vishnukowndinya]

hi sir, dose it mean that, if i have x1 x2 c3 x4 (c3 is a categorical) . At 1st i have to use all the numeric (x1 2 4) variables to build pca, then adding the cat var c3 my data changes as (pc1,pc2, c3) ??? or in the begining itself i have to convert the categorical c3 into (1, 0) and then include all my input vars to form a pcs????

[@modelmichael1972]

Awesome video. Every R enthusiast needs to keep an eye on your channel. Thank you and keep up with great work!

[@bkrai]

+Model Michael thanks👍

[@padhanewalaullu]

Sir, Can we get code file ?

[@mukeshchoudhary2842]

Great video.. What if we want to include factor-like "Control and Heat" for genotypes? Please suggest

[@bkrai]

It should work fine.

[@safeeqahmed3306]

Great video. I have one doubt. What does the stddev attribute of PC contain? Standard deviations of the variables are already in scale..so what does stddev represent? Thanks a lot

[@bkrai]

At what point in time do you see this?

[@safeeqahmed3306]

Bharatendra Rai sorry it’s sdev attribute of pc and in 9:48 while showing the summary of pc, I would like to know what the standard deviation row denote..thanks a lot

[@bkrai]

It is standard deviation related to principal components. It helps to estimate what percentage of variability is captured by each principal component.

[@safeeqahmed3306]

Bharatendra Rai thanks a lot. I understand this now

[@shapeletter]

What is the difference between using "scale." and "scale"? Is it in order to use z-score vs. min-max?

[@bkrai]

Here the code requires scale. to be used. It uses z-score.

[@shapeletter]

@@bkrai Okay, thanks! I will try it out! :)

[@bkrai]

Welcome!

[@mohammadj.shamim9342]

Dear Respected Sir, I wanted to install ggbiplot using the command you provided with us. but it gives me another message. The message is (Installation failed: SSL certificate problem: self signed certificate in certificate chain Warning message: Username parameter is deprecated. Please use vqv/ggbiplot) I used vqv/ggbiplot as well, but no good results. please guide me what shall I do?

[@bkrai]

Not sure what went wrong. May be some typo or something else. Probably you can try running commands using my R file.

[@safezonesharing914]

Thank you for your VDO. My R version is 3.5.1 and it cannot allow ggbiplot. Do you have any package instead of ggbiplot ?

[@bkrai]

Try installing it by running this line: install_github("ggbiplot", "vqv")

[@safezonesharing914]

@@bkrai Thank you for your kindly replying When I ran it, it would shown like this. Error in install_github("ggbiplot", "vqv") : could not find function "install_github"

[@ashishsangwan5925]

@@safezonesharing914 I'm also getting the same error

[@ashishsangwan5925]

@@safezonesharing914 try below command. It worked for me library(devtools) install_github("vqv/ggbiplot")

[@alexandrec.2939]

@@ashishsangwan5925 Arf, for few seconds I believed you were my saver ^^. But nope, your alternative didn't work as well

[@sebvangeli]

Great work! Thank you

[@parametersofstatistics2145]

Thanks sir .....can u please tell me how start learning on R from beginning?

[@bkrai]

You can start with this playlist: kzbin.info/aero/PL34t5iLfZddv8tJkZboegN6tmyh2-zr_T

[@ConeliusC33]

Your videos have been constant companions during the last months of my master thesis. It seemed as if every time I had to switch to another analysis technique you were allready waiting here. So thank you a lot for your guidance and clear explanations! The only thing I would appreciate would be if you could provide the basic R scripts. Even though the copying process might help with understanding each command due to step by step application, to type text of a tiny youtube screen shown in one half of my monitor to r studio in the other half is troublesome. Thanks!

[@bkrai]

Thanks for the feedback!

[@anigov]

Dear Sir..thanks for a wonderful video. I have some questions. 1) At 20:18, why did u choose to reorder by setosa? 2)Why did you choose to use data as trg and not training to build mymodel given that trg has predictions from training 3) Can PCA be used to choose k in kmeans. If so, how to go about it? Thanks again. Regards

[@scholars.home999]

Sir, can you please suggest how I can perform PCA on my Panel Data? -Regards

[@vishalaaa1]

Hi, Most of the people who are seeing the videos are new to data science. Please explain the parameters of each function. Just typing wont help them. The more detail and the more slow, the better the no of views. I myself was a trainer. The difference between 1000 views and a million views is the clarity and completeness

[@bkrai]

Thanks for the feedback!

[@murilocintra180]

Excellent demonstration of PCA, really helpful​. I just don't understand why in pc object, you use only training data instead of the entire data.

[@bkrai]

We only use training data so that we can later use test data to assess prediction model.

[@eniolaa77]

how do i get the file

[@bkrai]

here is the link: drive.google.com/open?id=0B5W8CO0Gb2GGYTQ2SWxkc2FCVGs

[@mamadououattara210]

Hi Dr, How to I use PCA to generate a score based on several variables? Regards