PCA or Principle Component Analysis is actually the way of finding variables which are similar and then extract the common data from the same variables so that data analysis can be done over less dimension but could result in better result.

Some more problems we can address using PCA -

- Find a new set of multivariate variables that are uncorrelated and explain as much variance as possible.

- If you put all the variables together in 1 matrix, find the best matrix created with fewer variables that explains the original data.

There is a big importance of Normalization before you do PCA -

'Normalization' is like if you have a large variance and other has small , PCA will be favored towards large variance. So if we have a variable in KM and if we increase the variance by converting it to CM , then PCA will start favoring the variable from No to 1st place.

http://stats.stackexchange.com/questions/69157/why-do-we-need-to-normalize-data-before-analysis

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