In October 2025, Safari was the most popular mobile internet browser in the United States, with a market share of over ** percent. Google Chrome came as a close second, with around **** percent of market share. U.S. browser market Considering Apple iPhone’s high user rate in the United States, it is no wonder that Safari, the browser pre-installed on every iPhone, is also widely used. When it comes to the overall browser market, however, Safari’s leading status gets lost: Chrome is the number one internet browser in the United States with a market share of about ** percent, while Safari trails as a second with around **** percent share. Safari lags even further behind in the desktop browser market, with only around **** percent share. This correlates to Apple’s standing in the PC market: ranked as number four in the market as of the first quarter of 2025, Apple’s Mac computers enjoy a relatively niche yet loyal user group. With a nearly ** percent share, Chrome is the dominating figure in the U.S. desktop browser market.

According to DeviceAtlas's data, Apple's iPhone 7 was the most popular iPhone model in 2020 with a ***** percent share in the overall global web usage . Among the other Apple smartphone models,iPhone 6S, iPhone 8, iPhone X and iPhone 6 enjoyed large share as well in that year. Apple was the vendor with the highest most smartphones sold globally as of the fourth quarter of 2020. This trend has been observed for the past couple of years, except for the third quarter of 2019, when Huawei generated more smartphone sales than Apple and therefore, ranked second.

 What part of Apple’s revenue comes from iPhones?  

iPhone sales composed more than half of Apple’s global revenue in the first quarter of 2021. This was a large increase from the quarter before, but the peak was reached in the first quarter of 2018, when nearly ** percent of the company’s revenue came from selling smartphones.

 Apple Pay   

With digital payments getting more and more popular, the number of Apple Pay users has also increased in recent years. Apple Pay is a mobile payment and digital wallet service by apple, initially released in October 2014. As of September 2019, nearly half of global iPhone users were using the service.

This dataset encompasses mobile web clickstream behavior on any browser, collected from over 150,000 triple-opt-in first-party US Daily Active Users (DAU). Use it for measurement, attribution or path to purchase and consumer journey understanding. Full URL deliverable available including searches with domain, path and parameter.

Tie web visits to app and location events using anonymized PanelistID for omnichannel consumer journey understanding.

The iPhone is a smartphone made by Apple that combines a computer, iPod, digital camera, and cellular phone into one device with a touchscreen interface. The iPhone runs the iOS operating system, and in 2021 when the iPhone 13 was introduced, it offered up to 1 TB of storage and a 12-megapixel camera. Different users different e-commerce websites like Flipkart, Amazon, meesho, etc, to find their desired products. In this dataset, we will see the iPhone prices, ratings, reviews, and its RAM on Amazon.

This dataset is for beginners who have started their journey in data analytics.

1) Data introduction • Apple-iphone-se-reviews dataset is a dataset that scrapes data from the Flipkart website using Selenium and BeautifulSoup links.

2) Data utilization (1)Apple-iphone-se-reviews data has characteristics that: • User ratings for Apple iPhone SE on Indian e-commerce website Flipkart are . We aim at NLP text classification through user ratings, review titles, and review text. (2)Apple-iphone-se-reviews data can be used to: • Rating prediction: You can support automated review analysis and summarization by developing machine learning models to predict ratings based on review text. • Product Improvement: Insights gained from reviews can help us identify common issues and areas for improvement in iPhone SE and guide product development and quality improvements.

I am a beginner in coding and a beginner in apple products prices. My data can be interested for those who want to acquire the newest iPhone for a reasonable price. I hope this data can help you to choose colour, capacity and other parameters or even make your own ml model to predict prices and compare it with on market.

This data were scraped from the web site 04/09/2020. I used BeautifulSoup and urlopen to do this.

I hope this data can help beginners like me to start with some data analysis and make beautiful data visualisation.

Mobile App Statistics (Apple iOS app store)

The ever-changing mobile landscape is a challenging space to navigate. . The percentage of mobile over desktop is only increasing. Android holds about 53.2% of the smartphone market, while iOS is 43%. To get more people to download your app, you need to make sure they can easily find your app. Mobile app analytics is a great way to understand the existing strategy to drive growth and retention of future user.

With million of apps around nowadays, the following data set has become very key to getting top trending apps in iOS app store. This data set contains more than 7000 Apple iOS mobile application details. The data was extracted from the iTunes Search API at the Apple Inc website. R and linux web scraping tools were used for this study.

Interactive full Shiny app can be seen here( https://multiscal.shinyapps.io/appStore/)

Data collection date (from API); July 2017

Dimension of the data set; 7197 rows and 16 columns

Content:

appleStore.csv

1. "id" : App ID

2. "track_name": App Name

3. "size_bytes": Size (in Bytes)

4. "currency": Currency Type

5. "price": Price amount

6. "rating_count_tot": User Rating counts (for all version)

7. "rating_count_ver": User Rating counts (for current version)

8. "user_rating" : Average User Rating value (for all version)

9. "user_rating_ver": Average User Rating value (for current version)

10. "ver" : Latest version code

11. "cont_rating": Content Rating

12. "prime_genre": Primary Genre

13. "sup_devices.num": Number of supporting devices

14. "ipadSc_urls.num": Number of screenshots showed for display

15. "lang.num": Number of supported languages

16. "vpp_lic": Vpp Device Based Licensing Enabled

appleStore_description.csv

1. id : App ID
2. track_name: Application name
3. size_bytes: Memory size (in Bytes)
4. app_desc: Application description

Acknowledgements

The data was extracted from the iTunes Search API at the Apple Inc website. R and linux web scraping tools were used for this study.

Inspiration

1. How does the App details contribute the user ratings?
2. Try to compare app statistics for different groups?

Reference: R package From github, with devtools::install_github("ramamet/applestoreR")

Licence

Copyright (c) 2018 Ramanathan Perumal

The mobile web analytics market, currently valued at $2756 million in 2025, is poised for significant growth, projected to expand at a compound annual growth rate (CAGR) of 8.6% from 2025 to 2033. This robust expansion is driven by several key factors. The increasing adoption of mobile devices globally fuels the demand for robust analytics solutions to understand user behavior and optimize website performance for mobile users. Furthermore, the rising sophistication of mobile web technologies, including the proliferation of progressive web apps (PWAs) and the increasing importance of mobile commerce (m-commerce), necessitates detailed analytics to track conversions, engagement metrics, and overall user experience. Competition among businesses to enhance their mobile presence and maximize return on investment (ROI) from mobile marketing efforts further drives market growth. The market segmentation, encompassing both Android and iOS platforms along with mobile app and mobile web page analytics, reflects the multifaceted nature of this sector and allows for specialized solutions tailored to individual client needs. Leading players such as Google, Facebook, Tencent, and others, leverage their existing technological infrastructure and vast user bases to dominate market share. The geographical distribution of the mobile web analytics market showcases a strong presence in North America and Europe, primarily due to the high level of digital maturity and technological adoption in these regions. However, substantial growth opportunities exist in rapidly developing economies across Asia-Pacific, particularly in countries like India and China, as smartphone penetration increases and businesses seek to capitalize on the expanding mobile user base. The market is also experiencing a growing need for advanced analytics capabilities, moving beyond basic website traffic data to encompass more sophisticated user segmentation, predictive analytics, and AI-driven insights. This shift towards data-driven decision-making will continue to shape the future of mobile web analytics and fuel further market expansion. The presence of established technology giants alongside innovative startups fosters competition and innovation, leading to a continuously evolving landscape of products and services.

App Tracking Transparency Key StatisticsATT Opt-In Rate by App CategoryATT Opt-In Rate by Game CategoryATT Opt-In Rate by CountryiOS Apps User TrackingiOS Apps Background Location AccessiOS Apps...

Discover the booming mobile web analytics market! Learn about its $4.5B+ size in 2025, projected 15% CAGR growth, key players (Google, Facebook, Tencent), and regional trends. Optimize your mobile strategy with our insightful market analysis.

This is a GPS dataset acquired from Google.

Google tracks the user’s device location through Google Maps, which also works on Android devices, the iPhone, and the web. It’s possible to see the Timeline from the user’s settings in the Google Maps app on Android or directly from the Google Timeline Website. It has detailed information such as when an individual is walking, driving, and flying. Such functionality of tracking can be enabled or disabled on demand by the user directly from the smartphone or via the website. Google has a Take Out service where the users can download all their data or select from the Google products they use the data they want to download. The dataset contains 120,847 instances from a period of 9 months or 253 unique days from February 2019 to October 2019 from a single user. The dataset comprises a pair of (latitude, and longitude), and a timestamp. All the data was delivered in a single CSV file. As the locations of this dataset are well known by the researchers, this dataset will be used as ground truth in many mobility studies.

Please cite the following papers in order to use the datasets:

T. Andrade, B. Cancela, and J. Gama, "Discovering locations and habits from human mobility data," Annals of Telecommunications, vol. 75, no. 9, pp. 505–521, 2020. 10.1007/s12243-020-00807-x (DOI)and T. Andrade, B. Cancela, and J. Gama, "From mobility data to habits and common pathways," Expert Systems, vol. 37, no. 6, p. e12627, 2020.10.1111/exsy.12627 (DOI)

The new mobile operating system iOS ** was installed on ** percent of the Apple devices that accessed the Apple App Store in March 2025, while the older iOS ** was running on ** percent of mobile devices and earlier releases on the remaining **** percent. Apple’s smartphone market share Apple held over ** percent share of the global smartphone sales market in 2024, but historically reports sales share increases during the fourth quarter because of the annual fall release of a new device and the holiday season. Demand for the iPhone ** and ** Plus, as well as iPhone ** Pro and Pro Max led to Apple announcing a sales figures of more than ** million units sold worldwide in the fourth quarter of 2024. In the first quarter of Apple's 2024 fiscal year, iPhone sales accounted for around ** percent of Apple's total revenue in the same quarter. The world at your fingertips Apple unveiled the very first iPhone and its supporting operating system (iOS 1) in 2007. It introduced an interface that allowed users to navigate the screen with their fingers, presenting a revolutionary touch keyboard that signaled a paradigm shift in the smartphone market.

📱 Global Mobile Reviews Dataset (2025 Edition)

🌍 Research-Based, Web-Scraped Global Review Collection

This dataset presents a curated collection of over 50,000 mobile phone reviews gathered through web scraping, market analysis, and content aggregation from multiple e-commerce and tech review platforms.
It covers eight countries and includes detailed user opinions, ratings, sentiment polarity, and pricing data across leading smartphone brands.

Each record captures customer experience holistically — spanning demographics, verified purchase details, multi-aspect ratings, and currency-adjusted pricing — making this dataset a powerful asset for research, NLP, and analytics.

🎯 Ideal For

• 🧠 Sentiment Analysis & NLP Modeling
• 💬 Text Classification & Review Mining
• 💰 Market Research & Pricing Analytics
• 📊 Consumer Behavior Studies
• 🤖 AI Model Training & Data Science Projects

🧩 Key Highlights

• 50,000+ mobile reviews scraped from top global sources
• Reviews across 8 major countries and multiple platforms
• Demographic data (customer name, age, location)
• Verified purchase flags for reliability
• Detailed product-level sub-ratings
• Pricing in both USD and local currencies
• Multilingual data support and country-specific sentiment distribution
• Professionally cleaned and normalized for research applications

📦 Brands Covered

🌐 Countries Represented

🧾 Example Record

📈 Research & Analytical Applications

• Sentiment Mining: Detect sentiment polarity in real-world review text
• Cross-Country Analysis: Compare satisfaction trends by region and currency
• Price–Rating Studies: Explore pricing elasticity and value perception
• Demographic Insights: Link sentiment to user age and verified purchase behavior
• Market Comparison: Understand brand trust and perception across regions

🧪 Data Collection & Research Approach

This dataset was compiled through an extensive research process combining web scraping, content aggregation, and analytical validation from multiple open and public review sources including:

• E-commerce platforms (e.g., Amazon, Flipkart, BestBuy, eBay)
• Tech review forums and discussion threads
• Mobile product feedback portals and blogs

Data was then: - Filtered for quality and consistency
- Mapped with real-world pricing and currency exchange rates
- Manually validated for sentiment balance and linguistic variation

⚠️ Note: All data is collected from publicly available review information and anonymized for research and educational use only.
No private or personally identifiable data was used or retained.

🧩 Research Summary

The dataset provides a multi-dimensional representation of the modern mobile ecosystem — integrating global pricing, sentiment trends, and demographic diversity to aid data scientists, researchers, and AI practitioners in building better understanding of customer perspectives.

Discover the booming tethering apps market! This in-depth analysis reveals a $2 billion market in 2025 projected to reach $6 billion by 2033, fueled by increasing smartphone usage and remote work trends. Explore market segments, key players (ClockworkMod, FoxFi, etc.), and regional growth projections for Android, iOS, and other tethering solutions.

IMPORTANT: Round 1 results are now released, check our website for the leaderboard. We now open Round 2 submissions!

1. Overview

We release two datasets that are part of the the Semantic Web Challenge on Mining the Web of HTML-embedded Product Data is co-located with the 19th International Semantic Web Conference (https://iswc2020.semanticweb.org/, 2-6 Nov 2020 at Athens, Greece). The datasets belong to two shared tasks related to product data mining on the Web: (1) product matching (linking) and (2) product classification. This event is organised by The University of Sheffield, The University of Mannheim and Amazon, and is open to anyone. Systems successfully beating the baseline of the respective task, will be invited to write a paper describing their method and system and present the method as a poster (and potentially also a short talk) at the ISWC2020 conference. Winners of each task will be awarded 500 euro as prize (partly sponsored by Peak Indicators, https://www.peakindicators.com/).

2. Task and dataset brief

The challenge organises two tasks, product matching and product categorisation.

i) Product Matching deals with identifying product offers on different websites that refer to the same real-world product (e.g., the same iPhone X model offered using different names/offer titles as well as different descriptions on various websites). A multi-million product offer corpus (16M) containing product offer clusters is released for the generation of training data. A validation set containing 1.1K offer pairs and a test set of 600 offer pairs will also be released. The goal of this task is to classify if the offer pairs in these datasets are match (i.e., referring to the same product) or non-match.

ii) Product classification deals with assigning predefined product category labels (which can be multiple levels) to product instances (e.g., iPhone X is a ‘SmartPhone’, and also ‘Electronics’). A training dataset containing 10K product offers, a validation set of 3K product offers and a test set of 3K product offers will be released. Each dataset contains product offers with their metadata (e.g., name, description, URL) and three classification labels each corresponding to a level in the GS1 Global Product Classification taxonomy. The goal is to classify these product offers into the pre-defined category labels.

All datasets are built based on structured data that was extracted from the Common Crawl (https://commoncrawl.org/) by the Web Data Commons project (http://webdatacommons.org/). Datasets can be found at: https://ir-ischool-uos.github.io/mwpd/

3. Resources and tools

The challenge will also release utility code (in Python) for processing the above datasets and scoring the system outputs. In addition, the following language resources for product-related data mining tasks: A text corpus of 150 million product offer descriptions Word embeddings trained on the above corpus

4. Challenge website

For details of the challenge please visit https://ir-ischool-uos.github.io/mwpd/

5. Organizing committee

Dr Ziqi Zhang (Information School, The University of Sheffield) Prof. Christian Bizer (Institute of Computer Science and Business Informatics, The Mannheim University) Dr Haiping Lu (Department of Computer Science, The University of Sheffield) Dr Jun Ma (Amazon Inc. Seattle, US) Prof. Paul Clough (Information School, The University of Sheffield & Peak Indicators) Ms Anna Primpeli (Institute of Computer Science and Business Informatics, The Mannheim University) Mr Ralph Peeters (Institute of Computer Science and Business Informatics, The Mannheim University) Mr. Abdulkareem Alqusair (Information School, The University of Sheffield)

6. Contact

To contact the organising committee please use the Google discussion group https://groups.google.com/forum/#!forum/mwpd2020

iPhone 14 📱 🐦 Tweets [11 July - Sept 9 2022 - 144k English] 📱 🐦

Updated on Sept 9th Includes sent tweets after launch

https://store.storeimages.cdn-apple.com/4668/as-images.apple.com/is/iphone-14-pro-finish-unselect-gallery-1-202209_GEO_EMEA?wid=5120&hei=2880&fmt=p-jpg&qlt=80&.v=1660754213188" alt="Photo by Apple">

Trying to do something useful and add a dataset here in Kaggle, and while there are over 90+ datasets for Elon, there's none yet for tweets about the upcoming iPhone 14. I'm interested in seeing what apple is up to this year, so I thought it could be interesting to deep dive into what people have been saying this month before the release, which was announced today by Apple. It will happen on September 7th.

The dataset has 144k tweets created between July 11th and Sept 9th. Tweets are in English. As the new iPhone was just announced, I plan on updating the dataset to include newer examples and maybe a few older ones to increase the number of samples in the dataset, at least until the first week of launch.

Columns Description

• [x] date_time - Date and Time tweet was sent
• [x] username - Username that sent the tweet
• [x] user_location - Location entered in the account location info on Twitter
• [x] user_description - Text added to "about" in account
• [x] verified - If the user has the "verified by Twitter" blue tick
• [x] followers_count - Number of Followers
• [x] following_count - Number of accounts followed by the person who sent the tweet
• [x] tweet_like_count - How many people liked the tweet
• [x] tweet_retweet_count - How many people retweeted the tweet
• [x] tweet_reply_count - How many people replied to that tweet
• [x] source - Where was the tweet sent from. The link has info if using iPhone, Android and others
• [x] tweet_text - Text sent in the tweet

Data and Utilization

Data was scrapped from Twitter and uploaded as is, no further process to data cleaning was performed, but the data from the tweets are in very good shape. I'd maybe recommend separating data and time and finding a way to change the source from links to the device name or website, depending on what you are interested in using the data for.

Usage suggestions - Data can be used to perform sentiment analysis, look at the geographical distribution, trends, spam x ham identification, and others.

Delve into the vibrant world of iPhone reselling with 'eBay iPhone Pricing Trends 2023', a rich dataset showcasing seller-driven prices for iPhone models like 11 Pro Max, 12 Pro Max, 13 Pro Max, 14 Pro Max, and XR. Ethically compiled, this data captures eBay's bustling market dynamics, offering a unique lens into how sellers price these high-demand Apple products. Ideal for data enthusiasts, this dataset is a gateway to insights on pricing strategy, market demand, and consumer trends. Elevate your market analysis with this authentic, eBay-sourced dataset, a window into the evolving secondary iPhone market. (Tool: Selenium has been used)

The iPhone was the most common smartphone type among Swedes in 2024, owned by ***** percent of the respondents taking part in a survey on smartphone ownership. Android smartphones were owned by ***** percent, while only **** percent of Swedes used a Samsung phone. iPhone versus Android Smartphone usage is getting more and more popular. A survey, conducted in 2019, found that ** percent of Swedish people aged between 16 and 25 used the internet daily via smartphones. The leading iPhone apps in Sweden, by number of downloads, were HBO Max, Melodifestivalen, and Talking Ben the Dog. They had approximately *** thousand, ** thousand and ** thousand downloads respectively. In contrast, the leading Android apps were Amazon Shopping, Subway Surfers, and Zen Match that year. They had roughly *** thousand, *** thousand and *** thousand downloads as of February 2022. What is important for Nordic citizens? Quality and coverage of the network when accessing the internet or data services was the leading feature that Nordic respondents (** percent) found important when owning a phone in 2017. This percentage grew in 2018, amounting to ** percent. In addition, ** percent of respondents in 2018 also said quality and coverage of the network for voice calls was important.

Smartprix Dataset Documentation

📌 About Smartprix

Smartprix is a leading Indian price comparison website that tracks products across e-commerce platforms. It specializes in electronics like smartphones, laptops, and gadgets, providing:
- Real-time price tracking
- Product specifications
- User reviews and ratings
- Deal alerts

This dataset contains smartphone specifications scraped from Smartprix, useful for price analysis, feature comparisons, and market research.

📊 Column Descriptions

🛠️ Potential Use Cases

1. Price Trend Analysis
2. Feature vs. Price Correlation
3. Brand Comparison (Apple vs. Samsung vs. OnePlus)
4. Recommender Systems

ℹ️ Data Notes

• Source: Smartprix Mobile Section

# Sample code to load the dataset
import pandas as pd
df = pd.read_csv("smartprix_phones.csv")
print(df.head())