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#1




Principal Component Analysis
Let me rephrase my earlier post. I am having trouble understanding Principal Component Analysis, which I understand is now part of an Associate exam. Would someone kindly share a simple numerical example in Excel or R so I can follow the calculation, and also explain what one then does with the numerical result?
Thank you, Jerry
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Thanks, Jerry 
#2




Elements of Statistical Learning may be helpful.
Also, there's a PCA Wiki page that may be informative. Finally, a perhaps interesting visual example is shown here. 
#3




see PAC's links above.
I always liked this application of PCA to Yield Curve analysis: https://www.moodysanalytics.com//me...Modelling.pdf The numerical results of PCA create new attributes for your data (each principal component is a new column for each data point). Common application of PCA for exploratory data analysis: calculate PCA, then use the top 2 components as axes for a scatterplot. See what patterns emerge and analyze for meaning (you'll have to figure out what the components mean) Last edited by BayesDeep; 01252020 at 11:24 PM.. 
#5




Man. Well said. PCA is becoming a credible approach to actuarial analysis.

#6




What in the who now?

#7




Quote:
A component is just a linear combination of your features. The first principal component is the combination of features that give the largest variance. The second principal component is the combination of features that give the largest variance while also being orthogonal to the first. This can continue for each component until your # of principal components is equal to your original number of features. As someone mentioned, if you like to play pretend, you can grab the first two principal components and make a 2D plot that will be hard to explain but will make a pretty picture nonetheless. The only real redeeming factor of Principal components is the orthogonality, which has some nice properties when doing a basic regression. Riley 
#8




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#9




Quote:
A key point I forgot to add is that while PCA reduces the dimensionality, it does not reduce the number of features needed, at least not natively. Riley 
#10




Quote:
https://www.rbloggers.com/indepth...expertvideos/ There are a couple short video lessons in Chapter 10 on principle component analysis. A lab in R demonstrating PCA is also provided. 
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