If our data is well suited for PCA we should be able to discard these components while retaining at least 70–80% of cumulative variance. Since an eigenvalues <1 would mean that the component actually explains less than a single explanatory variable we would like to discard those. Since we standardized our data and we now have the corresponding eigenvalues of each PC we can actually use these to draw a boundary for us. Right, so how many components do we want? We obviously want to be able to explain as much variance as possible but to do that we would need all 30 components, at the same time we want to reduce the number of dimensions so we definitely want less than 30! if we used the first 10 components we would be able to account for >95% of total variance in the data.
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