Let's look at a real-life example of the application of the PCA method. Import the libraries with which we will work:

Next, we read the train.csv file (from web), which contains data on house sales with the characteristics of houses and their prices:

Let's process our data. This process includes dropping many characteristics from the dataset (we will leave only 10 variables - this way it will be easier for us to work with the results obtained so that there are not too many characteristics), as well as data scaling:

Let's create a PCA model:

Now, to explain the results obtained, we will create a heat map of the factor loading. In the next section, we will learn why we need it.

In just a couple of steps, we reduced the dimension of the dataset from 10 characteristics to 3! In the next chapter, we will try to interpret the results of PCA.