Pass Task 8.1P: PCA dimensionality reduction

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()

detailed info available at: https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html

• PCA(n_components=2)

• 3D plot settings: (Please refer to prac7 for 3D plot examples)

from mpl_toolkits.mplot3d import Axes3D

fig = plt.figure(figsize=(10, 8))

cmap = plt.cm.get_cmap("Spectral")

ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=10, azim=10)

ax.scatter(x,y,z, c=cancer.target, cmap=cmap)

• Other settings of your choice

You are asked to:

• use StandardScaler() to first fit and transform the cancer.data,

• apply PCA (n_components=2) to fit and transform the scaled cancer.data set

• print the scaled dataset shape and PCA transformed dataset shape for comparison

• create 2D plot with the first principal component as x axis and the second principal component as y axis

• set proper xlabel, ylabel for the 2D plot

• print the PCA component shape and component values

• create a 3D plot with the first 3 features (as x,y and z) of the scaled cancer.data set

• create a 3D plot with the first principal component as x axis and the second principal component as y axis, no value for z axis

• set proper title for the two 3D plots

Sample output as shown in the following figures are for demonstration purposes only. Yours might be different from the provided.