[SOLVED] 代写 html security SIT384 Cyber security analytics

SIT384 Cyber security analytics
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
cmap = plt.cm.get_cmap(“Spectral”)
ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=10, azim=10) ax.scatter(x,y,z,…, 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.

Submission:
Submit the following files to OnTrack:
1. Your program source code (e.g. task8_1.py)
2. A screen shot of your program running
Check the following things before submitting: 1. Add proper comments to your code