Principal Component analysis reduces high dimensional data to lower dimensions while capturing maximum variability of the dataset.
This problem we face when analyzing higher-dimensional datasets is what commonly referred to as “ The curse of dimensionality”.
Now if we try to increase the number of variables it gets almost impossible for us to imagine a dimension higher than three-dimensions.
