We used PCA and received modified data, with a smaller dimension. What will be our next step?

As mentioned earlier, the data obtained is used in machine learning models, i.e. PCA basically acts only as a data processing step.

Once you have your data processed by PCA, you can use it in any machine learning model. It can be a model that solves the problem of classification, regression, clustering, etc. As an example, the use of PCA when working with images, because datasets are often large and with a lack of capacity, a dataset with more than 500,000 images can already become difficult to process.
A few examples of how PCA can be used on its own:

- Visualization of multidimensional data
- Information compression

And examples of when PCA is used as a data process:

- Data dimension reduction
- Noise reduction in data

In the following chapters, you will explore in detail some of the most common uses for PCA.

Choose the types of data that PCA can work with effectively:

Let's take a look at PCA (Principal Component Analysis) from afar. In this section, you will learn what PCA is, why this data processing method is needed and see the real problems it solves.

Learn about the design of the PCA algorithm. This section will show each stage in detail and simply.

Set up your first PCA model using the Python library and solve the challenge provided for you.

Get some insights with 2D and 3D visualization of PCA results.

It's time to find out how we can explain the results. What do the first, second and third components tell us? What can we use the data for in the future?

Select a few correct answers