Principle Component Analysis(PCA) | Part 3 | Code Example and Visualization

Рет қаралды 41,888

Күн бұрын

In this video, we provide a code example and visualization to showcase how to implement PCA in Python. Follow along and see the power of PCA in action, simplifying data and enhancing visualization for better insights.
Code used: github.com/campusx-official/1...
TASK PCA: colab.research.google.com/dri...
Task PCA Solution : colab.research.google.com/dri...
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⌚Time Stamps⌚
00:00 - Practical Example on MNIST Dataset
01:30 - PCA Code Demo
21:50 - 2D Visualization
28:50 - Explained Variance
30:48 - Finding Optimum number of principle components required
34:34 - PCA Code Demo
39:35 - When does PCA not work?
43:00 - Outro

Пікірлер: 77

@mridang2064 Жыл бұрын

Honestly you deserve Bharat Ratna . Teachers like you are rare as heck !! I thank the stars for finding your channel. If I ever make something of myself in this field I won't forget you and would help you in any way I can. Dhanyavaad dil se

@amitnakrani Жыл бұрын

To contribute, you can join his paid service. Just a thought.

@SwiftMind11 Жыл бұрын

@@amitnakrani you're right, we all should do the same. It will be the best form of appreciation to him as well as encouragement to produce more resources in future.

@vinayakmane7569 Жыл бұрын

bharat ratna ?

@arjunshankar1673 Жыл бұрын

@@vinayakmane7569 🤣🤣🤣🤣

@user-fo5uz8oe1s 3 ай бұрын

And madan puraskar too love from Nepal

@ankitbiswas8380 Жыл бұрын

PCA has never been clearer . The three videos of PCA in this playlist is very powerful and has definitely understand the PCA better

@Raj-gc2rc Жыл бұрын

Not entirely though. you can go much deeper by understanding the covariance matrix is symmetric positive semi-definite this implies that the eigenvectors are orthogonal and Eigenvalues are all real

@ankitbiswas8380 Жыл бұрын

@@Raj-gc2rc honestly not required any deeper than this for my regular professional data science work . Thanks for your concern though

@Raj-gc2rc Жыл бұрын

@@ankitbiswas8380 it depends on what work you are doing .... which application are you working on ?

@radhikawadhawan4235 Ай бұрын

A lot of beautiful statistical concepts were left from this video. eg why eigenvectors are orthogonal, etc.

@nakulmehta797 Ай бұрын

For the first time i can say that I am enjoying learning , especially coding .. All thanks to you sir .

@soumenkhatua4001 Жыл бұрын

You are not a teacher a you are absolutely genius.................

@arun5351 3 жыл бұрын

Great job mate! Can you please start time series and it's algorithms. In many of the interviews I attended, the client expectation is that time series is a fundamental concept and one should be adroit in time series problems. Surprisingly, very few trainers on KZfaq are exploring this topic and that too just on the surface. I request you to please start time series lectures as it's very intuitive topic and you're exceptional in explaining complex topics. Regards

@Raj-gc2rc Жыл бұрын

Are they expecting from us to know theory of random variables and random process ... like wide sense stationary , ergodic process in the time series interviews

@ahsanzafar4921 Жыл бұрын

In the last part of video, where you explained how to count the number of components to keep by plotting a graph, I want to add that it can be done very easilt, PCA n_components argument that takes number of components to create also takes in a float value like 0.80 or 0.90, which automatically tells PCA keep upto the number of components that explains this much variance f data, reference of is book "Hands on Machine Learning with Scikit Learn chapter PCA", Thank you, all three videos excellently cover the topic.

@jayeshparmar2603 7 ай бұрын

It was just a perfect explanation of pca, Loved it. Without pca on wine data accuracy score 94% with pca 99perc ❤

@itz_me_imraan02 7 ай бұрын

till now you are the best in explanation💚💚🔥...you dont skip even 1 single point

@gouravjaiswal4616 Жыл бұрын

You are the best I can arrure you. There is not a single channel on youtube on ML and data science that can beat you.

@santoshkamble9189 2 жыл бұрын

Awesome and information video, great job bro.. Looking for more from you on Deep learning as well.. Thanks a lot..

@thatsfantastic313 Жыл бұрын

I even forget to comment, your videos are that much interesting!!!!!! love from Pakistan.😍

@architasingh5659 Ай бұрын

This is really wonderful Explanation...tysm sir for providing such a valuable vedio

@alishaw9999 2 жыл бұрын

Wonder sir, you explanation is wonderful

@krithwal1997 2 жыл бұрын

Bhai ki explanation 🙌❤

@devadasdamodharan1511 8 ай бұрын

Awesome video. Thank you 😍

@ParthivShah 4 ай бұрын

Thank You Sir.

@sanaayakurup5453 Жыл бұрын

Brilliant as always. PCA can also be added as a pipeline component in the sklearn pipeline video that you made!

@kislaykrishna8918 2 жыл бұрын

thanku sir for such a great explanation 🙏

@avishinde2929 Жыл бұрын

great job sir ji Thank you so much

@vivaanyadav1st 8 ай бұрын

Nice Explanation ❤❤

@shaktijain8560 Жыл бұрын

Amazing!!

@muhammadmobeen8682 Жыл бұрын

Simply.. Awslme

@Naman_Bansal102 2 ай бұрын

Bhai Best Video Hai🥰🥰🥰🥰🥰🥰

@complexaayush9281 8 ай бұрын

amazing

@FindMultiBagger Жыл бұрын

Great 😃

@siddhigolatkar8558 Жыл бұрын

Thank you

@namanmodi7536 2 жыл бұрын

sir best video

@arshad1781 3 жыл бұрын

thanks

@AbcdAbcd-ol5hn Жыл бұрын

Thank you so much😭😭😭😭

@shivombhargava2166 Жыл бұрын

This guy is a Gem. Getting such amazing quality content for data science that too in Hindi is almost a miracle.

@shrutianand285 Жыл бұрын

Sir can you please make video on Binary particle swarm optimisation and how it is used for feature selection.

@radhikawadhawan4235 Ай бұрын

One of the main drawback of PCA is: you lose your original features and hence interpretability and explainability

@manojrangera5955 2 жыл бұрын

Sir.. Please can you tell me where I can find deployment of ml model using flask and heroku, preferred dl model... Please reply

@goyanii Жыл бұрын

you deserve Bharat Ratna

@PiyushASupe 2 жыл бұрын

How can we run online predictions on model trained using features extracted by PCA. How to apply pca transform in this case?

@mustafachenine7942 2 жыл бұрын

Is it possible to have an example of pictures to classify them into two categories? If the dimensions are reduced in pca and classification in knn is better , please

@souvikghosh6509 8 ай бұрын

One question remains, if we have a depolyed model, how the PCA would be applied on it?? Because, at that time there won't be any matrix (here we have test data so it can calculate test data's cov matrix's eigen vector and eigen value and multiply with the test data) Only observation points will come as vector.. the same question arises while doing the mean centering or standard scaling.. Please let me know if there is any clarity required in that question.

@tusharshukla9361 Жыл бұрын

Hey, PCA(n_components=3) is not working ??? showing this error: ValueError: n_components=3 must be between 0 and min(n_samples, n_features)=2 with svd_solver='full' (what to do please help)

@yashjain6372 Жыл бұрын

best

@kumji1922 Жыл бұрын

loved the visualization felt that happiness

@tanb13 Жыл бұрын

Sirjee, LDA and t-SNE to reh gaya PCA ke baad, pls uske videos ahi bana deejeye !

@Shisuiii69 Жыл бұрын

Sir last me Jo ap ne data ki example di thi k un data me help nhi krega tow wo plot kaise kre ge data pr Sirf y label data ko dekhna hota hai ya sare x features data pr plot kr k dekhna hoga k data ka shape Please tell me

@1981Praveer Жыл бұрын

#CampusX How would PCA will know which features should be excluded? Or, how could I know which features are used to process @16:44 ?

@abdulhannan-go8hl Жыл бұрын

features are selected by PCA on the basis of Eigen values and Eigen vectors, which is explained in the last video. We always select n_estimator where accuracy is higher. we can run the loop in intervals of 100,200,300 and so on to check where the n_estimator gives the best accuracy rate.

@wahabali828 Жыл бұрын

love from Pakistan

@ahsanulkyumsiam8774 4 ай бұрын

Sir, Kaggle code is not available here. Only previous video github link available.

@manojrangera5955 2 жыл бұрын

Please reply sir.... Can we use PCA to reduce Dimensionality of highly non-lenear dataset?

@sahilkadam9950 2 жыл бұрын

@anshulsharma7080 Жыл бұрын

Sir ye plotting ka code yd krna hota h kya?

@siddheswarmondal595 9 ай бұрын

PCA is applicable for continuous or discrete data ;can we use it for catagorical data in this video you use it for catagorical data which i think is not correct

@JustPython 7 ай бұрын

Sir, This specific class source code is not available on github. please share the source code.

@sidindian1982 Жыл бұрын

Sir Code & dataset(Mnist ) are missing , pls reload the same ;-)

@mellmiss8522 3 жыл бұрын

I need pca code in spyder 3.8

@descendantsoftheheroes_660 11 ай бұрын

sir where is topic LDA

@ytg6663 3 жыл бұрын

Sir , regression task me PCA apply ho sakta h kya ???

@souravsharma-ew6cp 2 жыл бұрын

Yes bro

@abdulhannan-go8hl Жыл бұрын

not in non-linear dataset but yes in linear dataset.

@historyofislam7509 Жыл бұрын

so difficult

@sodiqrafiu9072 3 жыл бұрын

English Please.............................

@campusx-official 3 жыл бұрын

Sir, the thing is I am much more comfortable teaching in hindi. So its a conscious decision from side. I am really sorry if you are facing inconvenience. I hope you understand

@sodiqrafiu9072 3 жыл бұрын

@@campusx-official Sure, I get you. Thanks anyways

@todaystrending1992 3 жыл бұрын

Sir, please continue in hindi

@ytg6663 3 жыл бұрын

If you want English why dont you switch to other videos, KZfaq is already filled with English ... We need this content in Hindi that is very rare..👍👍👍🔥🔥❤️❤️

@ytg6663 3 жыл бұрын

@@campusx-official Ha sir, Aap Hindi hee continue Rakhiye 😊😊😊🙂🙂🙂

@mellmiss8522 3 жыл бұрын

I need pca code in spyder 3.8