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malicious-website-features-2.4M

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malicious-website-features-2.4M / plots.py

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Create plots.py

15f79b5 11 months ago

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	import pandas as pd
	import matplotlib.pyplot as plt
	import seaborn as sns
	import numpy as np

	# Combine "combined_df.csv" and "combined_val_df.csv" into one dataframe
	df = pd.concat([pd.read_csv('phishing_features_train.csv'), pd.read_csv('phishing_features_val.csv')], ignore_index=True)

	# Define the columns to plot
	columns_to_plot = ['redirects', 'not_indexed_by_google', 'issuer', 'certificate_age', 'email_submission', 'request_url_percentage', 'url_anchor_percentage', 'meta_percentage', 'script_percentage', 'link_percentage', 'mouseover_changes', 'right_click_disabled', 'popup_window_has_text_field', 'use_iframe', 'has_suspicious_port', 'external_favicons', 'TTL', 'ip_address_count', 'TXT_record', 'check_sfh', 'count_domain_occurrences', 'domain_registeration_length', 'abnormal_url', 'age_of_domain', 'page_rank_decimal']

	# Create a list to store the file names of the saved plots
	file_names = []

	# Loop through the columns and create the scatterplot or barplot
	for column in columns_to_plot:
	if df[column].dtype == 'int64' or df[column].dtype == 'float64':
	fig, ax = plt.subplots()
	sns.regplot(x=column, y='is_malicious', data=df, ax=ax)
	corr_coef = df[[column, 'is_malicious']].corr().iloc[0,1]
	ax.set_title(f'{column} vs is_malicious\nCorrelation Coefficient: {corr_coef:.2f}')
	file_name = f'{column}_scatterplot.png'
	plt.savefig(file_name)
	file_names.append(file_name)
	elif df[column].dtype == 'object':
	fig, ax = plt.subplots()
	if (df[column] == "None").sum() > 0:
	sns.countplot(x=column, hue='is_malicious', data=df[df[column] == "None"], ax=ax)
	ax.set_title(f'{column} (null) vs is_malicious')
	file_name = f'{column}_null_barplot.png'
	plt.savefig(file_name)
	file_names.append(file_name)
	sns.countplot(x=column, hue='is_malicious', data=df, ax=ax)
	ax.set_title(f'{column} (all) vs is_malicious')
	file_name = f'{column}_all_barplot.png'
	plt.savefig(file_name)
	file_names.append(file_name)

	# Create a figure with subplots to combine the saved plots
	num_plots = len(file_names)
	num_rows = int(np.ceil(num_plots/2))
	fig, axs = plt.subplots(num_rows, 2, figsize=(20, 5*num_rows))
	for i, file_name in enumerate(file_names):
	row = i // 2
	col = i % 2
	img = plt.imread(file_name)
	axs[row, col].imshow(img)
	axs[row, col].axis('off')
	if num_plots % 2 == 1:
	axs[num_rows-1, 1].axis('off')
	plt.tight_layout()
	plt.savefig('correlation_coefficient.png')