Thanks for the video! The code framework was very useful to me! For 3d scatter plot of 3 PCs, I slightly tweaked to: pc1=score(:,1); pc2=score(:,2); pc3=score(:,3); scatter3(pc1,pc2,pc3,10,idx,'filled') I also used my own labelled data, so I changed the identity idx to my "GasType" column Idx=x(:,"GasType"); idx=table2array(Idx);
Hello, thanks for the video. Now I'm studying pca algothrim and find your videos. I have a question, you didn't use 'solar-radiation' data, and you said we can predict this data after this work. How can i find none values in 'solar-radiation' data?
Hello raul , pc1 and pc2 are basically the principle components , please refer the prerequisite videos present in the description box in -order , that will help you to understand this concept in better way
Obviously work-around is possible , try to write the code which will automate the process, you have to write own code based on situation , that's where fun of programming :-)
worst video, I was expecting PCA, you explained every thing except PCA. In 9 minute video titled PCA you explained 7 minutes the miscellaneous code, In last 2 mintues hurriedly explained PCA.
Hello Dr Mudassir Rafi, thank you for sharing your feedback. I understand that you were expecting a more focused explanation of PCA in the video. However, it's important to note that the video you watched was specifically designed to showcase a practical application of PCA using real data in MATLAB i.e. the intention was to provide a tangible example and highlight the steps involved in implementing PCA with actual data. If you are looking for a more in-depth theoretical explanation of PCA, I would recommend watching my previous videos on the topic(the links are available in description box). In those videos, I thoroughly explain the core concepts, provide intuitive insights, and delve into the mathematical principles behind PCA. Here are the links to the videos that cover complete PCA , starting from the theoretical aspects of PCA to practical to interview questions: ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 1)PCA Theoretical Intuition :ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-xMTyAL4f6S4.html 2)Understand crucks of PCA with practical: the ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-PfROP0VQ3wU.html 3)Applying PCA on real dataset : ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-zBFno27oeTg.html 4)Message Hiding using PCA: ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-IrQJoSi3dpE.html 5)How many dimensions?:ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-iMHTgwTFJjQ.html 6)Understand & interpret result of pca in MATLAB(Part 1): ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-u1kUB4Cs9nI.html 7)Understand & interpret result of pca in MATLAB(Part 2):ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-VCSpqnfufd8.html 8)Feature Scaling in PCA: ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-BFBWZpuBYJI.html 9)PCA Interview question :ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-_Svym7xaipc.html Same way videos are arranged in the ML playlist -- ru-vid.com/group/PLjfRmoYoxpNoaZmR2OTVrh-72YzLZBlJ2 I hope these videos will offer the detailed explanation you were seeking regarding PCA.
