No general method works in every situation for choosing the optimal number of principal components. As a result, the data can be described by fewer features than the original, which will ease the statistical inference. However, the ones which account for most of the data variation are retained in the analysis while the rest is omitted. The components can be created as many as the number of original variables. Principal components are the linear transformations of the original variables in the dataset. If you wonder how to conduct the analysis in R or Python, see our tutorials: How to Use PCA in R and How to Use PCA in Python.īefore getting started, let’s explain what the principal components exactly are. If you want to learn more about what PCA does and when and why to use it see our extensive tutorial: PCA Explained. Now we can conduct a PCA to explain the data variation in a more compact and interpretable way.
As seen in Table 1, the dataset contains 11 numerical variables showing the features of each car model.
Let’s have a quick look at what the first few rows of data look like. For illustration, we will use the mtcars dataset to perform a PCA.
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