PCA (Principle Component Analysis) is a dimensionality

Task description:
PCA (Principle Component Analysis) is a dimensionality reduction technique that projects the data
into a lower dimensional space. It can be used to reduce high dimensional data into 2 or 3
dimensions so that we can visualize and hopefully understand the data better.
In this task, you use PCA to reduce the dimensionality of a given dataset and visualize the data.
You are given:
• Breast cancer dataset which can be retrieved from:
from sklearn.datasets import load_breast_cancer
cancer = load_breast_cancer()

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