if you have lot of moviesthat' lot of dimensions and you can' really wrap your head around more than because that' what we grew up to evolve within you might have some sort of data that has many different features that you care about you knowin moment we'll look at an example of flowers that we want to classifyand that classification is based on different measurements of the flowers those different featuresthose different measurements can represent dimensionswhich againis very hard to visualize for this reasondimensionality reduction techniques exist to find way to reduce higher dimensional information into lower dimensional information not only can that make it easier to look atand classify thingsbut it can also be useful for things like compressing data soby preserving the maximum amount of variancewhile we reduce the number of dimensionswe're more compactly representing dataset very common application of dimensionality reduction is not just for visualizationbut also for compressionand for feature extraction we'll talk about that little bit more in moment very simple example of dimensionality reduction can be thought of as -means clusteringso you knowfor examplewe might start off with many points that represent many different dimensions in dataset butultimatelywe can boil that down to different centroidsand your distance to those centroids that' one way of boiling data down to lower dimensional representation principal component analysis usuallywhen people talk about dimensionality reductionthey're talking about technique called principal component analysis this is much more-fancy techniqueit gets into some pretty involved mathematics butat high-levelall you need to know is that it takes higher dimensional data spaceand it finds planes within that data space and higher dimensions