The document outlines the use of Python with the Olivetti Faces dataset in Google Colab, focusing on dimensionality reduction using PCA. It involves importing necessary libraries, loading the dataset, applying PCA to reduce dimensions, and preparing the transformed data for model training. Additionally, it prompts the visualization of images to observe differences among individuals.

1. Use python to answer: Done on google colabortory.
Dataset used: Olivetti Faces Dataset Description
import numpy as np
from sklearn import datasets
from sklearn.decomposition import PCA
# Load the dataset
faces = datasets.fetch_olivetti_faces()
data = faces.data # Input data or input features
target = faces.target # Label or Class or Variable to be predicted
# Apply PCA to reduce the dimensionality of the data
pca = PCA(n_components=100, whiten=True)
X = pca.fit_transform(data) ## X Represents the data and you will use for model training
# Size of the datset
X.shape # (Number of images, input feature)
Question 1:
Visualize some (at least five) of the images in the dataset. Are there any noticeable differences
between the images of different individuals? (Hint: You can use imshow() method to visualise
images)
#Your code will go here