We figured out the implementation of the PCA algorithm using the `numpy` library. `Scikit-learn` can let us start using this method with just one line of code:
```python
from sklearn.decomposition import PCA
pca_model = PCA(n_components = 2)
```

`PCA` is a `scikit-learn` library class. It contains more than 5 arguments, but we are most interested in only one - `n_components`. This argument is responsible for the number of main components that we want to get. The only condition is that the number of components must, of course, be equal to or less than the variables in the dataset.
The `PCA` class contains 2 main methods that we will use: `fit` and `transform`. The `fit()` method loads the data into the class, and the `transform()` method transforms it, and we get the result of the `PCA` algorithm. If we want to combine these 2 operations, use the `fit_transform()` method:
```python
pca_model = PCA(n_components = 2)

# fit() and transform()
pca_model.fit(X)
X_reduced = pca_model.transform(X)

# fit_transform()
X_reduced = pca_model.fit_transform(X)
```

If we want to get the components that the algorithm has calculated, call the `.components_` attribute:
```python
print(pca_model.components_)
```

Principal component analysis is one of the most popular data dimensionality reduction algorithms. This course will help to understand how to create PCA models and analyze the results. Let's start!

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 us.

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?

Scikit-learn for PCA

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