Now you have become familiar with all stages of the PCA algorithm: data standardization, calculation of the covariance matrix, calculation of eigenvalues, eigenvectors, formation of a feature vector, and subsequent application of the results to the data.

So, as you can see, the main components are linear combinations of the original variables from the dataset. This is one of the main ideas that is important to remember. Also, as you may have noticed, we only used PCA to work with continuous data. In the following sections you will find out why.

You have seen that Python has all the tools you need to implement the Principal Component Method step by step. As you have seen in previous chapters, creating a PCA model can be done in 1 line, but here we wanted to show you a little more detail behind it all.

In the following chapters, you will be able to solve the problem of data dimensionality reduction on a large dataset.

Do you think PCA will perform well with a dataset that contains continuous and categorical data?

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Yes, PCA can work with any type of data

PCA can work with such data, but the result can be low, other methods have been developed for this

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