Task description: PCA ( Principle Component Analysis) is = 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 dateset 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) 30 plot settings: (Please refer to prac7 for 30 plot examples) from mpl_toolkits.mplotid import Axe:3D fig = plt.figure(figsize=[10, 8)] cmap = pit.cm.get_cmap["Spectral") ax = Axes3D(fig, rect=[0, 0, .85, 1), elev=10, azim=10) Ex.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 20 plot with the first principal component as x axis and the second principal component as y axis set proper xlabel, ylabel for the 20 plot print the PCA component shape and component values create = 30 plot with the first 3 features (as xy and =] of the scalled cancer.data set create = 30 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 30 plots Sample output as shown in the following figures are for demonstration purposes only. Yours might be different from the provided