labs/Module-3/3.PCA.ipynb

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# Start by importing the necessary libraries and reading in the dataset
from sklearn.decomposition import PCA
import pandas as pd
data = pd.read_csv("file_pe_headers.csv", sep=",")
X = data.drop(["Name", "Malware"], axis=1).to_numpy()

Analysis

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#Standardize the dataset as it is neccessary before applying PCA
from sklearn.preprocessing import StandardScaler
X_standardized = StandardScaler().fit_transform(X)

print(X_standardized.shape)

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# Instantiate a PCA instance use it to reduce the dimensionality of our data
pca = PCA()
pca.fit_transform(X_standardized)

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# Assess the effectiveness of your dimensionality reduction
print(pca.explained_variance_ratio_)

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sum(pca.explained_variance_ratio_[0:50])

Reduce dimensional

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pca_reduce = PCA(n_components=25)

pca_reduce.fit_transform(X_standardized)

X_standardized

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X_pca = pca_reduce.transform(X_standardized)

print(X_pca.shape)

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