What is principal component analysis? Explain the sort of problems you would use PCA for

Data Science, AI and ML
#1

In its most basic form, PCA entails projecting higher-dimensional data (for example, three dimensions) into a smaller space (eg. 2 dimensions). This reduces the number of dimensions in the data (from three to two) while retaining all of the original variables in the model.

PCA is widely used for data compression, which reduces the amount of memory required and speeds up the process, as well as visualization, which makes it simpler to summarise data.