Code:

Packet_Features_Generator.py & Features.py

To run this code:

pkt_features.py [-h] -i TXTFILE [-x X] [-y Y] [-z Z] [-ml] [-s S] -j

-h, --help show this help message and exit -i TXTFILE input text file -x X Add first X number of total packets as features. -y Y Add first Y number of negative packets as features. -z Z Add first Z number of positive packets as features. -ml Output to text file all websites in the format of websiteNumber1,feature1,feature2,... -s S Generate samples using size s. -j

Purpose:

Turns a text file containing lists of incomeing and outgoing network packet sizes into separate website objects with associative features.

Uses Features.py to calcualte the features.

startMachineLearning.sh & machineLearning.py

To run this code:

bash startMachineLearning.sh

This code then runs machineLearning.py in a tmux session with the nessisary file paths and flags

Options (to be edited within this file):

--evaluate-only to test 5 fold cross validation accuracy

--test-scaling-normalization to test 6 different combinations of scalers and normalizers

Note: once the best combination is determined, it should be added to the data_preprocessing function in machineLearning.py for future use

--grid-search to test the best grid search hyperparameters - note: the possible hyperparameters must be added to train_model under 'if not evaluateOnly:' - once best hyperparameters are determined, add them to train_model under 'if evaluateOnly:'

Purpose:

Using the .ml file generated by Packet_Features_Generator.py & Features.py, this program trains a RandomForest Classifier on the provided data and provides results using cross validation. These results include the best scaling and normailzation options for each data set as well as the best grid search hyperparameters based on the provided ranges.

Data

Encrypted network traffic was collected on an isolated computer visiting different Wikipedia and New York Times articles, different Google search queres (collected in the form of their autocomplete results and their results page), and different actions taken on a Virtual Reality head set.

Data for this experiment was stored and analyzed in the form of a txt file for each experiment which contains:

First number is a classification number to denote what website, query, or vr action is taking place.

The remaining numbers in each line denote:

The size of a packet,

and the direction it is traveling.

negative numbers denote incoming packets

positive numbers denote outgoing packets

Figure 4 Data

This data uses specific lines from the Virtual Reality.txt file.

The action 'LongText Search' refers to a user searching for "Saint Basils Cathedral" with text in the Wander app.

The action 'ShortText Search' refers to a user searching for "Mexico" with text in the Wander app.

The .xlsx and .csv file are identical

Each file includes (from right to left):

The origional packet data,

each line of data organized from smallest to largest packet size in order to calculate the mean and standard deviation of each packet capture,

and the final Cumulative Distrubution Function (CDF) caluclation that generated the Figure 4 Graph.

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This Website Statistics dataset has four resources showing usage of the Lincolnshire Open Data website. Web analytics terms used in each resource are defined in their accompanying Metadata file.

• Website Usage Statistics: This document shows a statistical summary of usage of the Lincolnshire Open Data site for the latest calendar year.

• Website Statistics Summary: This dataset shows a website statistics summary for the Lincolnshire Open Data site for the latest calendar year.

• Webpage Statistics: This dataset shows statistics for individual Webpages on the Lincolnshire Open Data site by calendar year.

• Dataset Statistics: This dataset shows cumulative totals for Datasets on the Lincolnshire Open Data site that have also been published on the national Open Data site Data.Gov.UK - see the Source link.

  Note: Website and Webpage statistics (the first three resources above) show only UK users, and exclude API calls (automated requests for datasets). The Dataset Statistics are confined to users with javascript enabled, which excludes web crawlers and API calls.

These Website Statistics resources are updated annually in January by the Lincolnshire County Council Business Intelligence team. For any enquiries about the information contact opendata@lincolnshire.gov.uk.

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Mobile accounts for approximately half of web traffic worldwide. In the last quarter of 2024, mobile devices (excluding tablets) generated 62.54 percent of global website traffic. Mobiles and smartphones consistently hoovered around the 50 percent mark since the beginning of 2017, before surpassing it in 2020. Mobile traffic Due to low infrastructure and financial restraints, many emerging digital markets skipped the desktop internet phase entirely and moved straight onto mobile internet via smartphone and tablet devices. India is a prime example of a market with a significant mobile-first online population. Other countries with a significant share of mobile internet traffic include Nigeria, Ghana and Kenya. In most African markets, mobile accounts for more than half of the web traffic. By contrast, mobile only makes up around 45.49 percent of online traffic in the United States. Mobile usage The most popular mobile internet activities worldwide include watching movies or videos online, e-mail usage and accessing social media. Apps are a very popular way to watch video on the go and the most-downloaded entertainment apps in the Apple App Store are Netflix, Tencent Video and Amazon Prime Video.

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Difference uses Google Analytics as the Baseline. Results based on Paired t-Test for Hypotheses Supported.

In November 2024, Google.com was the most popular website worldwide with 136 billion average monthly visits. The online platform has held the top spot as the most popular website since June 2010, when it pulled ahead of Yahoo into first place. Second-ranked YouTube generated more than 72.8 billion monthly visits in the measured period. The internet leaders: search, social, and e-commerce Social networks, search engines, and e-commerce websites shape the online experience as we know it. While Google leads the global online search market by far, YouTube and Facebook have become the world’s most popular websites for user generated content, solidifying Alphabet’s and Meta’s leadership over the online landscape. Meanwhile, websites such as Amazon and eBay generate millions in profits from the sale and distribution of goods, making the e-market sector an integral part of the global retail scene. What is next for online content? Powering social media and websites like Reddit and Wikipedia, user-generated content keeps moving the internet’s engines. However, the rise of generative artificial intelligence will bring significant changes to how online content is produced and handled. ChatGPT is already transforming how online search is performed, and news of Google's 2024 deal for licensing Reddit content to train large language models (LLMs) signal that the internet is likely to go through a new revolution. While AI's impact on the online market might bring both opportunities and challenges, effective content management will remain crucial for profitability on the web.

Context

The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website.

Content

The sample dataset contains Google Analytics 360 data from the Google Merchandise Store, a real ecommerce store. The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website. It includes the following kinds of information:

Traffic source data: information about where website visitors originate. This includes data about organic traffic, paid search traffic, display traffic, etc. Content data: information about the behavior of users on the site. This includes the URLs of pages that visitors look at, how they interact with content, etc. Transactional data: information about the transactions that occur on the Google Merchandise Store website.

Fork this kernel to get started.

Acknowledgements

Data from: https://bigquery.cloud.google.com/table/bigquery-public-data:google_analytics_sample.ga_sessions_20170801

Banner Photo by Edho Pratama from Unsplash.

Inspiration

What is the total number of transactions generated per device browser in July 2017?

The real bounce rate is defined as the percentage of visits with a single pageview. What was the real bounce rate per traffic source?

What was the average number of product pageviews for users who made a purchase in July 2017?

What was the average number of product pageviews for users who did not make a purchase in July 2017?

What was the average total transactions per user that made a purchase in July 2017?

What is the average amount of money spent per session in July 2017?

What is the sequence of pages viewed?

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The backlink checker tool market is experiencing robust growth, driven by the increasing importance of SEO and digital marketing for businesses of all sizes. The market's expansion is fueled by several factors, including the rising adoption of cloud-based solutions, the need for enhanced website authority assessment, and the growing complexity of search engine algorithms. Small and medium-sized enterprises (SMEs) are a significant market segment, adopting these tools to compete effectively against larger players. Large enterprises, however, represent a substantial revenue stream due to their higher budgets and more sophisticated SEO needs. The market is further segmented by deployment type – cloud-based solutions are gaining traction due to their accessibility and scalability. While on-premise solutions still maintain a niche presence for businesses with stringent security requirements, the overall market trend clearly favors the cloud. The competitive landscape is highly dynamic, with both established players like Ahrefs and SEMrush and emerging companies vying for market share. This competitive environment fosters innovation and pushes for continuous improvement in the features and capabilities of these tools. Geographical analysis shows strong market presence in North America and Europe, followed by the Asia-Pacific region experiencing rapid growth. This is primarily due to the increasing digitalization and e-commerce adoption in developing economies within this region. Forecasts indicate continued market expansion, with a projected CAGR of approximately 15% from 2025 to 2033, suggesting a significant potential for continued investment and growth within the backlink checker tool market. The forecast period (2025-2033) anticipates continued market expansion, influenced by factors like improved tool accuracy, increasing demand for competitive analysis features, and ongoing developments in SEO techniques. The integration of AI and machine learning into backlink checker tools is another key factor driving growth by offering more sophisticated insights and automating aspects of backlink analysis. The restraints on market growth primarily stem from the high cost of advanced tools, the availability of free (though often limited) alternatives, and the need for continuous updates to remain compatible with evolving search engine algorithms. However, these restraints are unlikely to significantly impede the overall growth trajectory, given the vital role backlink analysis plays in modern SEO strategies and the growing demand for sophisticated tools to streamline this process. The market shows promising prospects across all regions, with continued penetration in established markets and significant potential for expansion in emerging economies.

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The global website speed and performance test tool market size was valued at USD 1.84 billion in 2022 and is projected to reach USD 5.52 billion by 2033, exhibiting a CAGR of 12.0% during the forecast period. The escalating demand for website performance optimization services, the surge in website traffic, and the proliferation of mobile devices drive market growth. Moreover, the growing adoption of cloud-based solutions and the increasing preference for online shopping fuel market expansion. Key players in the website speed and performance test tool market include Pingdom, Yellow Lab Tools, Alerta, Sematext, Domsignal, Dareboost, New Relic, Google PageSpeed Insights, KeyCDN Website Speed Test, Yslow, Uptrends, GTmetrix, Site24x7, Datadog, Catchpoint WebPageTest, Dotcom-Monitor, Lighthouse, WebPagetest, and Load Impact. These companies are focusing on offering advanced features and enhancing the capabilities of their tools to gain a competitive edge. The market is fragmented, with several players offering a wide range of solutions catering to different customer needs and industries.

The global market for website speed and performance test tools is experiencing robust growth, driven by the increasing importance of online user experience and the rise of e-commerce. The market's expansion is fueled by several key factors. Businesses are increasingly recognizing the direct correlation between website speed and conversion rates, customer satisfaction, and search engine rankings. This has led to a surge in demand for sophisticated tools that provide detailed performance insights, helping businesses identify and address bottlenecks. The shift towards cloud-based solutions simplifies deployment and scalability, further accelerating market adoption. The diverse range of tools available caters to various needs, from simple website speed tests for individuals to comprehensive performance monitoring platforms for large enterprises. Market segmentation by application (personal, enterprise, other) and type (cloud-based, on-premises) reflects this diverse user base and the evolving technological landscape. While the on-premises segment might be facing some stagnation due to the ease of use and scalability offered by cloud-based solutions, the overall market shows significant growth potential. The competitive landscape is characterized by a mix of established players and emerging startups. Established companies such as Pingdom, GTmetrix, and New Relic offer comprehensive solutions with extensive feature sets, while smaller players often focus on niche areas or provide more specialized services. The market is expected to witness further consolidation and innovation in the coming years, with a focus on artificial intelligence (AI)-powered insights and integration with other digital marketing tools. Geographical expansion, particularly in developing economies with growing internet penetration, presents significant opportunities for market growth. While data privacy regulations and the complexity of some tools could pose challenges, the overall market outlook for website speed and performance testing remains highly positive, driven by the enduring need for optimizing website performance and enhancing the online user experience. We project a continued strong CAGR in the coming years, reflecting this positive market dynamic.