Dataset Overview

This dataset provides detailed information on website traffic, including page views, session duration, bounce rate, traffic source, time spent on page, previous visits, and conversion rate.

Dataset Description

• Page Views: The number of pages viewed during a session.
• Session Duration: The total duration of the session in minutes.
• Bounce Rate: The percentage of visitors who navigate away from the site after viewing only one page.
• Traffic Source: The origin of the traffic (e.g., Organic, Social, Paid).
• Time on Page: The amount of time spent on the specific page.
• Previous Visits: The number of previous visits by the same visitor.
• Conversion Rate: The percentage of visitors who completed a desired action (e.g., making a purchase).

Data Summary

• Total Records: 2000
• Total Features: 7

Key Features

1. Page Views: This feature indicates the engagement level of the visitors by showing how many pages they visit during their session.
2. Session Duration: This feature measures the length of time a visitor stays on the website, which can indicate the quality of the content.
3. Bounce Rate: A critical metric for understanding user behavior. A high bounce rate may indicate that visitors are not finding what they are looking for.
4. Traffic Source: Understanding where your traffic comes from can help in optimizing marketing strategies.
5. Time on Page: This helps in analyzing which pages are retaining visitors' attention the most.
6. Previous Visits: This can be used to analyze the loyalty of visitors and the effectiveness of retention strategies.
7. Conversion Rate: The ultimate metric for measuring the effectiveness of the website in achieving its goals.

Usage

This dataset can be used for various analyses such as:

• Identifying key drivers of engagement and conversion.
• Analyzing the effectiveness of different traffic sources.
• Understanding user behavior patterns and optimizing the website accordingly.
• Improving marketing strategies based on traffic source performance.
• Enhancing user experience by analyzing time spent on different pages.

Acknowledgments

This dataset was generated for educational purposes and is not from a real website. It serves as a tool for learning data analysis and machine learning techniques.

In February 2026, Google.com was the most visited website worldwide, with 88.46 billion visits. The platform has maintained its leading position since June 2010, when it surpassed Yahoo to take first place. YouTube ranked second during the same period, recording 45.07 billion monthly visits. 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) signals 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.

In March 2024, Google.com was the leading website in the United States. The search platform accounted for over 19 percent of desktop web traffic in the United States, ahead of second-ranked YouTube.com with 10.71 percent.

Context

The data presented here was obtained in a Kali Machine from University of Cincinnati,Cincinnati,OHIO by carrying out packet captures for 1 hour during the evening on Oct 9th,2023 using Wireshark.This dataset consists of 394137 instances were obtained and stored in a CSV (Comma Separated Values) file.This large dataset could be used utilised for different machine learning applications for instance classification of Network traffic,Network performance monitoring,Network Security Management , Network Traffic Management ,network intrusion detection and anomaly detection.

The dataset can be used for a variety of machine learning tasks, such as network intrusion detection, traffic classification, and anomaly detection.

Content :

This network traffic dataset consists of 7 features.Each instance contains the information of source and destination IP addresses, The majority of the properties are numeric in nature, however there are also nominal and date kinds due to the Timestamp.

The network traffic flow statistics (No. Time Source Destination Protocol Length Info) were obtained using Wireshark (https://www.wireshark.org/).

Dataset Columns:

No : Number of Instance. Timestamp : Timestamp of instance of network traffic Source IP: IP address of Source Destination IP: IP address of Destination Portocol: Protocol used by the instance Length: Length of Instance Info: Information of Traffic Instance

Acknowledgements :

I would like thank University of Cincinnati for giving the infrastructure for generation of network traffic data set.

Ravikumar Gattu , Susmitha Choppadandi

Inspiration : This dataset goes beyond the majority of network traffic classification datasets, which only identify the type of application (WWW, DNS, ICMP,ARP,RARP) that an IP flow contains. Instead, it generates machine learning models that can identify specific applications (like Tiktok,Wikipedia,Instagram,Youtube,Websites,Blogs etc.) from IP flow statistics (there are currently 25 applications in total).

**Dataset License: ** CC0: Public Domain

Dataset Usages : This dataset can be used for different machine learning applications in the field of cybersecurity such as classification of Network traffic,Network performance monitoring,Network Security Management , Network Traffic Management ,network intrusion detection and anomaly detection.

ML techniques benefits from this Dataset :

This dataset is highly useful because it consists of 394137 instances of network traffic data obtained by using the 25 applications on a public,private and Enterprise networks.Also,the dataset consists of very important features that can be used for most of the applications of Machine learning in cybersecurity.Here are few of the potential machine learning applications that could be benefited from this dataset are :

1. Network Performance Monitoring : This large network traffic data set can be utilised for analysing the network traffic to identifying the network patterns in the network .This help in designing the network security algorithms for minimise the network probelms.

2. Anamoly Detection : Large network traffic dataset can be utilised training the machine learning models for finding the irregularitues in the traffic which could help identify the cyber attacks.

3.Network Intrusion Detection : This large dataset could be utilised for machine algorithms training and designing the models for detection of the traffic issues,Malicious traffic network attacks and DOS attacks as well.

In August 2025, Google.com was the most visited website worldwide, attracting approximately 5.66 billion unique monthly visitors. YouTube.com ranked second, with an estimated 2.98 billion unique visitors. Both platforms also held the top positions globally in terms of total website visits.

Column Descriptions

• Domain: Name of domain.
• Visits: Number of monthly visitors (in Billion).
• Desktop Share: % of visitors who uses a mobile desktop.
• Mobile Share: % of visitors who uses a mobile phone.
• MoM: % change in traffic as a percentage of the previous month's traffic stats.
• YoY: % change in traffic for the past 12 months as a percentage of the previous 12 months’ traffic stats.
• Main Traffic Source: Direct/Referral/Search/Social/Paid

Data Source: Open .Trends Last Update: November 2022

It’s not really surprising to know that most of the internet traffic comes from mobile devices. Yet, I wouldn’t have believed this 10 or 15 years back. Sure, mobile devices were becoming popular, but the adoption rates had a sudden jump in the past decade. A quick analysis of statistics...

In the second quarter of 2025, mobile devices (excluding tablets) accounted for 62.54 percent of global website traffic. Since consistently maintaining a share of around 50 percent beginning in 2017, mobile usage surpassed this threshold in 2020 and has demonstrated steady growth in its dominance of global web access. 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.

This dataset provides detailed insights into website traffic metrics and user engagement statistics, collected from SimilarWeb. The data includes information on various websites, such as rank, category, average visit duration, pages per visit, and bounce rate. This data aims to facilitate an understanding of online behavior and performance trends across different sectors, making it a valuable resource for researchers, marketers, and data analysts. The dataset is ideal for exploring patterns in web traffic and user interaction and conducting comparative analyses across various website categories.

Important Warning: Running this code within Kaggle may result in a ban, as scraping activities are prohibited on the platform. There is no guarantee that any ban will be lifted, as Kaggle staff may interpret scraping as a denial-of-service attack. Although I have implemented measures to reduce server load, such as adding sleep intervals, it is advisable to run this code locally to ensure compliance with Kaggle's policies.

Website Traffic Analysis

Website traffic analysis is the process of monitoring and evaluating the visitors to a website. It provides insights into how users are interacting with the site, where they are coming from, which pages they visit most often, and how long they stay. By analyzing this data, businesses can understand user behavior, improve site performance, and optimize content to increase engagement and conversions.

Key metrics include the number of visitors, page views, bounce rate, traffic sources (organic, referral, direct), and geographic location. Website traffic analysis is essential for enhancing SEO, refining marketing strategies, and boosting overall user experience.

General data recollected for the studio " Analysis of the Quantitative Impact of Social Networks on Web Traffic of Cybermedia in the 27 Countries of the European Union". Four research questions are posed: what percentage of the total web traffic generated by cybermedia in the European Union comes from social networks? Is said percentage higher or lower than that provided through direct traffic and through the use of search engines via SEO positioning? Which social networks have a greater impact? And is there any degree of relationship between the specific weight of social networks in the web traffic of a cybermedia and circumstances such as the average duration of the user's visit, the number of page views or the bounce rate understood in its formal aspect of not performing any kind of interaction on the visited page beyond reading its content? To answer these questions, we have first proceeded to a selection of the cybermedia with the highest web traffic of the 27 countries that are currently part of the European Union after the United Kingdom left on December 31, 2020. In each nation we have selected five media using a combination of the global web traffic metrics provided by the tools Alexa (https://www.alexa.com/), which ceased to be operational on May 1, 2022, and SimilarWeb (https:// www.similarweb.com/). We have not used local metrics by country since the results obtained with these first two tools were sufficiently significant and our objective is not to establish a ranking of cybermedia by nation but to examine the relevance of social networks in their web traffic. In all cases, cybermedia whose property corresponds to a journalistic company have been selected, ruling out those belonging to telecommunications portals or service providers; in some cases they correspond to classic information companies (both newspapers and televisions) while in others they refer to digital natives, without this circumstance affecting the nature of the research proposed.
Below we have proceeded to examine the web traffic data of said cybermedia. The period corresponding to the months of October, November and December 2021 and January, February and March 2022 has been selected. We believe that this six-month stretch allows possible one-time variations to be overcome for a month, reinforcing the precision of the data obtained. To secure this data, we have used the SimilarWeb tool, currently the most precise tool that exists when examining the web traffic of a portal, although it is limited to that coming from desktops and laptops, without taking into account those that come from mobile devices, currently impossible to determine with existing measurement tools on the market. It includes:

Web traffic general data: average visit duration, pages per visit and bounce rate Web traffic origin by country Percentage of traffic generated from social media over total web traffic Distribution of web traffic generated from social networks Comparison of web traffic generated from social netwoks with direct and search procedures

Web traffic statistics for the top 2000 most visited pages on nyc.gov by month.

We provide foot traffic data across the web with 30+ enriched attributes, including demographics, devices, web activity, intent and foot traffic insights. We help marketers, agencies, and platforms build precise foot traffic audience segments, optimize foot traffic targeting, attribute locations, and understand cross-device journeys. Our continuously updated foot traffic datasets deliver real-time foot traffic insights that power smarter location-based campaigns and future-ready strategies.

Leverage our foot traffic data solutions for the following use cases: - Foot Traffic Data Validation & Model Building - Cultural & Seasonal Foot Traffic Insights - Targeted, Data-Driven Foot Traffic Advertising - Foot Traffic & Location-Based Targeting - Trial & Partnership Transparency

With AdPreference, expect the following key benefits through our partnership: - Augment Foot Traffic Data Attributes - Enrich CRM - Personalize Foot Traffic Audiences - Fraud Prevention - Foot Traffic Audience Curation

Access the largest and most customizable foot traffic data segments with AdPreference today. Supercharge your needs with unique and enriched foot traffic data not found anywhere else.

For more information, please visit https://www.adpreference.co/

We provide foot traffic data across the web with 30+ enriched attributes, including demographics, devices, web activity, intent and foot traffic insights. We help marketers, agencies, and platforms build precise foot traffic audience segments, optimize foot traffic targeting, attribute locations, and understand cross-device journeys. Our continuously updated foot traffic datasets deliver real-time foot traffic insights that power smarter location-based campaigns and future-ready strategies.

Leverage our foot traffic data solutions for the following use cases: - Foot Traffic Data Validation & Model Building - Cultural & Seasonal Foot Traffic Insights - Targeted, Data-Driven Foot Traffic Advertising - Foot Traffic & Location-Based Targeting - Trial & Partnership Transparency

With AdPreference, expect the following key benefits through our partnership: - Augment Foot Traffic Data Attributes - Enrich CRM - Personalize Foot Traffic Audiences - Fraud Prevention - Foot Traffic Audience Curation

Access the largest and most customizable foot traffic data segments with AdPreference today. Supercharge your needs with unique and enriched foot traffic data not found anywhere else.

For more information, please visit https://www.adpreference.co/

According to a report published by DataReportal, as of November 2023, the most visited website in Thailand was Google.com with approximately *** million monthly visits. This was followed by Youtube.com with around *** million monthly visits in that year.

Context

This dataset includes some of the basic information of the websites we daily use. While scrapping this info, I learned quite a lot in R programming, system speed, memory usage etc. and developed my niche in Web Scrapping. It took about 4-5 hrs for scrapping this data through my system (4GB RAM) and nearly about 4-5 days working out my idea through this project.

Content

The dataset contains Top 50 ranked sites from each 191 countries along with their traffic (global) rank. Here, country_rank represent the traffic rank of that site within the country, and traffic_rank represent the global traffic rank of that site.

Since most of the columns meaning can be derived from their name itself, its pretty much straight forward to understand this dataset. However, there are some instances of confusion which I would like to explain in here:

1) most of the numeric values are in character format, hence, contain spaces which you might need to clean on.

2) There are multiple instances of same website. for.e.g. Yahoo. com is present in 179 rows within this dataset. This is due to their different country rank in each country.

3)The information provided in this dataset is for the top 50 websites in 191 countries as on 25th May 2017 and is subjected to change in future time due to the dynamic structure of ranking.

4) The dataset inactual contains 9540 rows instead of 9550(50*191 rows). This was due to the unavailability of information for 10 websites.

PS: in case if there are anymore queries, comment on this, I'll add an answer to that in above list.

Acknowledgements

I wouldn't have done this without the help of others. I've scrapped this information from publicly available (open to all) websites namely: 1) http://data.danetsoft.com/ 2) http://www.alexa.com/topsites , of which i'm highly grateful. I truly appreciate and thanks the owner of these sites for providing us with the information that I included today in this dataset.

Inspiration

I feel that there this a lot of scope for exploring & visualization this dataset to find out the trends in the attributes of these websites across countries. Also, one could try predicting the traffic(global) rank being a dependent factor on the other attributes of the website. In any case, this dataset will help you find out the popular sites in your area.

Largest publicly available multi-city traffic dataset with 23,541 stationary detectors across 40 cities worldwide. Rich source for traffic dynamics research and urban mobility studies.

elegant-most.com is ranked # in US with 0 Traffic. Categories: . Learn more about website traffic, market share, and more!

YouTube flows

We provide mobility data across the web with 30+ enriched attributes, including demographics, devices, web activity, intent and mobility insights. We help marketers, agencies, and platforms build precise mobility audience segments, optimize mobility targeting, attribute locations, and understand cross-device journeys. Our continuously updated mobility datasets deliver real-time mobility insights that power smarter mobility-based campaigns and future-ready strategies.

Leverage our mobility data solutions for the following use cases: - Mobility Data Validation & Model Building - Cultural & Seasonal Campaign Mobility Insights - Targeted, Data-Driven Mobility Advertising - Travel & Location-Based Targeting - Trial & Partnership Transparency

With AdPreference, expect the following key benefits through our partnership: - Augment Mobility Data Attributes - Enrich CRM - Personalize Mobility Audiences - Fraud Prevention - Mobility Audience Curation

Access the largest and most customizable mobility data segments with AdPreference today. Supercharge your needs with unique and enriched mobility data not found anywhere else.

For more information, please visit https://www.adpreference.co/