At the very beginning, we touched on the topic of interpreting the results obtained. First of all, it is worth mentioning that there is no universal method, there is only the possibility of expert evaluation, which depends entirely on how well you know the data you are working with.
We have already mentioned the factor loading matrix. This matrix allows you to evaluate the contribution of each variable to the principal components. Formally, this means that each component is represented by a linear combination of initial variables Xn and coefficients wn:

Thus, the loadings - are the coefficients wn of a linear combination of the initial variables from which the principal components are built.
We have already visualized the factor loading matrix, but now you will get acquainted with a more convenient option. With the help of the `pca` library, you can easily create a visualization of loadings in 2D and 3D space:

We create the graphs obtained above using the following code:

```python
from pca import pca

model = pca(n_components = 3)
results = model.fit_transform(X, row_labels = y)

fig, ax = model.plot()

# Scatter 2D model
fig, ax = model.scatter()
fig, ax = model.biplot(n_feat = 4)

# Scatter 3D model
fig, ax = model.scatter()
fig, ax = model.biplot3d()
```

If you want to specify labels for data classes, add an argument `row_labels` with y value for the function `fit_transform()`.

What does the different direction of loadings on the biplots mean?

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?