Can you help me with this python code? please include the printed scaled dataset before the graphs. Are you able to comm

[answerhappygod]

Can you help me with this python code? please include the
printed scaled dataset before the graphs. Are you able to comment
the code so I can understand it line-by-line?

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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 0

Original shape: (569, 30) Reduced shape: (569, 2) PCA component shape: (2, 30) PCA components: [[ 0.21890244 0.10372458 0.22753729 0.22099499 0.14258969 0.23928535 0.25840048 0.26085376 0.13816696 0.06436335 0.20597878 0.01742803 0.21132592 0.20286964 0.01453145 0.17039345 0.15358979 0.1834174 0.04249842 0.10256832 0.22799663 0.10446933 0.23663968 0.22487053 0.12795256 0.21009588 0.22876753 0.25088597 0.12290456 0.13178394] [-0.23385713 -0.05970609 -0.21518136 -0.23107671 0.18611302 0.15189161 0.06016536 -0.0347675 0.19034877 0.36657547 -0.10555215 0.08997968 -0.08945723 -0.15229263 0.20443045 0.2327159 0.19720728 0.13032156 0.183848 0.28009203 -0.21986638 -0.0454673 -0.19987843 -0.21935186 0.17230435 0.14359317 0.09796411 -0.00825724 0.14188335 0.27533947]] 12.5 malignant A benign 10.0 7.5 5.0 Second principal component 2.5 0.0 -2.5 -5.0 -7.5 0 10 15 5 First principal component

malignant first 3 features of scaled X 3 N 2 0 -1 \\\\\\\ -2 ܘ ܠܨ ܐܘܢܐ -2 1 2 3

malignant first two principal components of X after PCA transformation 0.04 0.02 0.00 -0.02 -0.04 III - 5 10 15 -7.5 -5.0 -2.5 0.0 2.5 5.0 7.5 10.0 12.5