The number of Apple iPhone unit sales dramatically increased between 2007 and 2024. Indeed, in 2007, when the iPhone was first introduced, Apple shipped around **** million smartphones. By 2024, this number reached over ***** million units. The newest models and iPhone’s lasting popularity Apple has ventured into its 17th smartphone generation with its Phone ** lineup, which, released in September 2025, includes the **, ** Plus, ** Pro and Pro Max. Powered by the A19 bionic chip and running on iOS **, these models present improved displays, cameras, and functionalities. On the one hand, such features come, however, with hefty price tags, namely, an average of ***** U.S. dollars. On the other hand, they contribute to making Apple among the leading smartphone vendors worldwide, along with Samsung and Xiaomi. In the first quarter of 2024, Samsung shipped over ** million smartphones, while Apple recorded shipments of roughly ** million units. Success of Apple’s other products Apart from the iPhone, which is Apple’s most profitable product, Apple is also the inventor of other heavy-weight players in the consumer electronics market. The Mac computer and the iPad, like the iPhone, are both pioneers in their respective markets and have helped popularize the use of PCs and tablets. The iPad is especially successful, having remained as the largest vendor in the tablet market ever since its debut. The hottest new Apple gadget is undoubtedly the Apple Watch, which is a line of smartwatches that has fitness tracking capabilities and can be integrated via iOS with other Apple products and services. The Apple Watch has also been staying ahead of other smart watch vendors since its initial release and secures around ** percent of the market share as of the latest quarter.

Apple is one of the most influential and recognisable brands in the world, responsible for the rise of the smartphone with the iPhone. Valued at over $2 trillion in 2021, it is also the most valuable...

iPhone Reviews from Amazon.com

30,000+ User-Submitted Reviews

By Crawl Feeds [source]

About this dataset

This 30,000+ reviews dataset for Apple iPhone from Amazon.com provides insights and comprehensive opinion data that can be used to understand current customer sentiment towards the product. With helpful_count as one of the columns, this dataset provides an opportunity to find out which reviews are most helpful for customers and highlights the key areas of improvement for other brands in a similar product range. Exceptional review ratings and detailed text reviews give readers an idea about why customers liked or disliked the product, providing valuable market feedback information such as what went wrong (or right). Alongside this, knowledge about where a review was made gives better context on whether comments should be taken lightly or with more pressing importance. An invaluable resource for industry stakeholders and researchers alike, use this dataset to gain a clearer picture of customer satisfaction surrounding Apple's latest release - The iPhone!

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How to use the dataset

This dataset contains over 30,000 reviews for the Apple iPhone from Amazon.com. It includes information such as the product name, helpful count, total comments, URL of the review, review country, date and time of the review, rating of the product given by reviewer, product company name and profile name. You can use this dataset to analyze customer feedback about the Apple iPhone from Amazon.

To get started with this dataset you should first read through each column and understand what it represents. Once you are familiar with each column then you can start exploring the data further by filtering out particular reviews or performing a sentiment analysis on particular reviews using tools such as Python's Natural Language Toolkit (NLTK). You could also look at analyzing trends in customer ratings over time or breaking down customer feedback into gender specific segments to gain more insights about user preferences.

You can also group reviews based on their geographical location and look at regional differences in user opinion towards a particular product feature or implementation style which may indicate alterations in usability/ technicalities that need to be addressed along with other factors such as cultural influence which may have an effect on user opinion towards a certain brand/product feature etc. This info could be used to inform your marketing strategies across different parts of your target market region thus providing more targeted results while creating ad campaigns aimed at driving sales for the aforementioned products/brands-helping improve ROI performance efficiently!

Lastly if you are looking for insights particularly regarding Apple’s competitors-it would be useful for you to analyze comparative feedback between customers regarding similar competitive brands/products allowing potential investments pivoting around stronger performers!

We hope this guide provides some useful insight into how to use this dataset effectively from Amazon mobile phones reviews set! Have fun exploring!!

Research Ideas

• To train a sentiment analysis model to better understand customers’ attitudes towards Apple's iPhone.
• To analyze the review comments and look for correlations between certain words and ratings, in order to gain insights on how customers perceive the phone based on their experiences with it.
• Create a combination of product reviews with video reviews from YouTube in order to provide potential buyers a more comprehensive overview of the features, performance and beauty of the iPhone before purchasing it

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: apple_iphone_11_reviews.csv | Column name | Description | |:--------------------|:-------------------------------------------------------------| | product | The product being reviewed. (String) | | helpful_count | The number of people who found the review helpful. (Integer) | | total_comments | The total number of comments on the review. (Integer) | | url | The URL of the review post. (String) | | review_country | The country from which the review was posted. (Strin...

In the fourth quarter of its 2025 fiscal year, Apple generated around ***billion U.S. dollars in revenue from the sales of iPhones. Apple iPhone revenue The Apple iPhone is one of the biggest success stories in the smartphone industry. Since its introduction to the market in 2007, Apple has sold more than *** billion units worldwide. As of the third quarter of 2024, the Apple iPhone’s market share of new smartphone sales was over ** percent. Much of its accomplishments can be attributed to Apple’s ability to keep the product competitive throughout the years, with new releases and updates. Apple iPhone growth The iPhone has shown to be a crucial product for Apple, considering that the iPhone’s share of the company’s total revenue has consistently grown over the years. In the first quarter of 2009, the iPhone sales were responsible for about ********* of Apple’s revenue. In the third quarter of FY 2024, this figure reached a high of roughly ** percent, equating to less than ** billion U.S. dollars in that quarter. In terms of units sold, Apple went from around **** million units in 2010 to about *** million in 2023, but registered a peak in the fourth quarter of 2020 with more than ** million iPhones sold worldwide.

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.

This dataset provides a comprehensive collection of information about all the latest smartphones available in the market as of the current time.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13571604%2Fb608498b1cf7f70b9a22952566197db6%2FScreenshot%202023-08-02%20003740.png?generation=1690961033930490&alt=media" alt="">

The dataset was created by web scraping reputable online sources to gather accurate and up-to-date information about various smartphone models, their specifications, features, and pricing.

Percentage of smartphone users by selected smartphone use habits in a typical day.

The global number of smartphone users in was forecast to continuously increase between 2024 and 2029 by in total *** billion users (+***** percent). After the fifteenth consecutive increasing year, the smartphone user base is estimated to reach *** billion users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smartphone users in countries like the Americas and Asia.

These datasets provide a comprehensive overview of Apple's financial performance by product and geographical segments from 2010 to 2024. The data is compiled directly from the company's annual 10-K filings with the U.S. Securities and Exchange Commission (SEC), ensuring a high degree of reliability and accuracy.

Dataset 1: Apple Sales by Product Segment This dataset details Apple's net sales across its major product and service categories. It shows the evolution of Apple's business model from a hardware-centric company to one with a significant and growing services component. The columns represent different business lines that have changed over time:

iPhone, Mac, iPad: Core hardware product categories.

iPod: An early product category whose sales data was eventually reclassified into a broader segment.

Other Products / Wearables, Home & Accessories: This category has evolved. Prior to 2015, this was a more general "Other Products" category. In later years, it was expanded and renamed to include products like the Apple Watch, AirPods, and HomePod, reflecting their growing strategic importance.

Services: This segment includes revenue from the App Store, Apple Music, iCloud, Apple Pay, and other digital services. Its consistent growth highlights Apple's successful diversification strategy.

Dataset 2: Apple Sales by Geographic Segment This dataset breaks down Apple's net sales by major global regions, providing insight into the company's international market penetration and performance. It includes data for:

Americas: Includes North and South America.

Europe: Includes European countries, as well as Africa, India, and the Middle East.

Greater China: Includes mainland China, Hong Kong, and Taiwan.

Japan: A distinct and long-standing key market for Apple.

Rest of Asia Pacific: Includes Australia and various other countries in Asia.

Example Use Cases These datasets can be used for a wide range of analytical purposes, including:

Trend Analysis and Forecasting: Analysts can use this data to identify long-term trends in Apple's sales. For example, by plotting the data, one could easily visualize the rapid growth of the Services segment, or the decline of the iPod as a standalone product. This can help in forecasting future revenue and strategic planning.

Market Share Analysis: By comparing Apple's sales data to the total market size for smartphones, personal computers, or tablets, analysts can estimate Apple's market share over time. This can be done on a global or regional basis to identify areas of strength and weakness.

Strategic Decision Making: Business leaders can use the geographic sales data to evaluate the success of market-specific strategies. For instance, a company could compare the growth rates in Greater China and Europe to determine which region offers more potential for a new product launch.

Comparative Analysis: The data can be used to compare Apple's performance against its competitors like Samsung or Google. By normalizing the data, one could analyze which company has a more diversified revenue stream or a stronger presence in specific international markets. I

Apple is one of the leaders in smartphone design and innovation. So naturally, people want to buy Apple devices. Various factors have been deemed to affect the purchase decision of buyers in the past, and present time isn't any different either. This dataset will try to predict this exact behavior from over 400 samples taken online.

Smartphones have become crucial in people's everyday lives, including in the medical field. However, as people become close to their smartphones, this leads easily to overuse. Overuse leads to fatigue due to lack of sleep, depressive symptoms, and social relationship failure, and in the case of adolescents, it hinders academic achievement. Self-control solutions are needed, and effective tools can be developed through behavioral analysis. Therefore, the aim of this study was to investigate the determinants of users' intentions to use m-Health for smartphone overuse interventions. A research model was based on TAM and UTAUT, which were modified to be applied to the case of smartphone overuse. The studied population consisted of 400 randomly selected smartphone users aged from 19 to 60 years in South Korea. Structural equation modeling was conducted between variables to test the hypotheses using a 95% confidence interval. Perceived ease of use had a very strong direct positive association with perceived usefulness, and perceived usefulness had a very strong direct positive association with behavioral intention to use. Resistance to change had a direct positive association with behavioral intention to use and, lastly, social norm had a very strong direct positive association with behavioral intention to use. The findings that perceived ease of use influenced perceived usefulness, that perceived usefulness influenced behavioral intention to use, and social norm influenced behavioral intention to use were in accordance with prior related research. Other results that were not consistent with previous research imply that these are unique behavioral findings regarding smartphone overuse. This research identifies the critical factors that need to be considered when implementing systems or solutions in the future for tackling the issue of smartphone overuse.

This dataset lists the most popular smartphones of 2023 in India gathered from Flipkart, one of the largest e-commerce platforms in the country.

The dataset can be used to identify which smartphones and price ranges are preferred by users, the impact of discounts, and how ratings vary.

Column Description

1. title - the name of the smartphone + color of the model + memory + RAM
2. price - price of the smartphone after discount
3. prod_rating - rating of the smartphone
4. rating_count - the number of people
5. discount - the discount offered in percentage
6. seller_rating - rating of the seller of that particular smartphone as rated by their buyer on the whole seller experience

Project Ideas

1) Extract information from the title like brand name, model, color, memory, and RAM. Use different strategies and see which works the best.

2) Correlation analysis - the price of the smartphone could be influenced by rating, number of ratings, discount, and seller rating.

3) Regression - build a regression model to predict the price of a smartphone, by using variables such as "prod_rating," "rating_count," "discount," and "seller_rating" as independent.

4) Visualizations - Get creative with visualizations, create an interactive dashboard, and create forecast charts.

Check out my other dataset on top-rated TV shows: https://www.kaggle.com/datasets/titassaha/top-rated-tv-shows

I write articles on data analysis and analytics, techniques, and document my learning process on my blog - https://emptyjar.in

Thanks.

There are a total of 17 questions in the survey, addressing the following categories:Internet useMobile phone use (smartphones & basic voice/SMS phones)Awareness and use of WikipediaGeneral demographicsThe survey collected 2500 total responses, representing populations in 5 geographical regions served by 3 mobile Iraqi operators. 3 language choices (Arabic, English, Kurdish) were provided.Here are the main questions this survey was designed to answer. However, analyzing the full data set allows you to conduct more in-depth data explorations and gain meaningful insights beyond the points presented here.What is the actual number of people who use the internet?(Real-world behavior makes this difficult to measure from industry reports, since people might have access to the internet through school, friends, internet cafés, public Wifi, etc.)For internet users: What do people mostly use the internet for?For non-internet users: Why not use the internet?How many people use smartphones?Do people with smartphones use the internet from just Wifi? Or just cellular service?How many people think that they don’t use the internet, but still use Facebook or WhatsApp?How many people have heard of Wikipedia? What do they use it for? How often?If they have heard of Wikipedia, but aren’t using it, why not?Compared to previous phone surveys in other countries, the 2017 Iraq phone survey presented new questions.What are people’s awareness of other major internet brands in comparison to Wikipedia?Can people find online content in their preferred language?How does data cost impact internet use?

This dataset contains clean data about smartphones📱 collected from Flipkart. It gives you detailed information about things like the names of the phones, their prices in Rs., how good people rated them overall, how many ratings they got, how many reviews were written, how much RAM and ROM they have, if you can add more storage, the size of the display in centimeters and inches, what kind of display technology they use, and more. This cleaned-up data is easy to use and understand, making it perfect for exploring and learning about different smartphones available on Flipkart.

The number of smartphone users in the Philippines was forecast to increase between 2024 and 2029 by in total 5.6 million users (+7.29 percent). This overall increase does not happen continuously, notably not in 2026, 2027, 2028 and 2029. The smartphone user base is estimated to amount to 82.33 million users in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

This dataset provides detailed information about various smartphones available on the e-commerce platform Flipkart as of July 2023. The dataset is in CSV format and contains the following columns:

img_link: This column contains the URL link to the image of each smartphone. It can be used to retrieve and display the corresponding image for each smartphone.

phone_name: This column contains the name or model of each smartphone. It provides a unique identifier for each device in the dataset.

avg_rating: This column represents the average rating of each smartphone on Flipkart. It indicates the overall customer satisfaction level based on user ratings. The rating scale typically ranges from 1 to 5 stars, with 5 being the highest rating.

total_rating: This column indicates the total number of people who have rated each smartphone on Flipkart. It provides an understanding of the popularity and feedback from customers who have shared their ratings.

total_reviews: This column represents the total number of reviews available for each smartphone on Flipkart. It provides insights into the level of engagement and the amount of user-generated content related to each device.

discounted_price: This column contains the discounted price of each smartphone in Indian Rupees (INR). It represents the current selling price of the device after applying any applicable discounts or promotional offers.

actual_price: This column displays the actual or original price of each smartphone in Indian Rupees (INR) before any discounts. It provides a reference point for the discounted price and helps users understand the amount of savings or price reduction available.

The dataset is valuable for conducting various analyses related to smartphones available on Flipkart. Researchers, data scientists, and analysts can use this dataset to explore trends in customer ratings, reviews, pricing, and discounts. They can also perform market research, brand comparisons, sentiment analysis, and other studies related to the smartphone industry.

Please note that this dataset is specific to Flipkart and represents the smartphone market as of July 2023. It can be used to gain insights into customer preferences, pricing strategies, and overall market dynamics in the context of Flipkart's smartphone offerings.

The number of smartphone users in France was forecast to continuously increase between 2024 and 2029 by in total 3.2 million users (+5.96 percent). After the fifteenth consecutive increasing year, the smartphone user base is estimated to reach 56.89 million users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smartphone users in countries like Belgium and Luxembourg.

📊 About the Dataset

Technology adoption has been evolving rapidly, shaping industries and consumer behaviors worldwide. This dataset provides insights into global gadget consumption trends from 2015 to 2025, covering smartphones, laptops, gaming consoles, smartwatches, and 5G penetration rates.

🎯 Use Cases

• 📈 Market research & consumer behavior analysis
• 🔮 Forecasting future tech adoption trends
• ♻️ Studying the impact of e-waste generation
• 🌐 Understanding 5G penetration across different countries

📑 Column Descriptions

Objective: Clinical voice quality assessments typically use external microphones meeting technical standards for instrumental assessment of voice. As smartphones advance, this study aimed to determine their suitability for voice recordings through a systematic review with meta-analysis.Method: Three database searches were conducted, ranging from their inception to December 2024, as well as a manual search. Cross-sectional studies were included on widely used clinical acoustic voice quality measures of the software Praat (i.e., jitter, shimmer, harmonics-to-noise ratio [HNR], smoothed cepstral peak prominence [CPPS], and acoustic voice quality index [AVQI]).Results: We found 10 eligible research studies with a total of 379 participants who were simultaneously compared between a clinical recording system (CRS) and different smartphones by Apple and Samsung products. All included studies focused on individuals with vocally healthy voices, while four of the studies also included those with voice disorders. In comparison with CRS, iPhones revealed significant differences and large effect sizes in HNR (mean difference of 2.20, 95% CI [0.59, 3.82], p = .008, Cohen’s d = 2.54) and in AVQI (mean difference of −0.53, 95% CI [−1.00, −0.06], p = .027, Cohen’s d = −1.99), but in the direct comparison between Apple and Samsung mobile device recordings, significant differences and large effect sizes were found in jitter (mean difference of −0.17, 95% CI −0.27, −0.08, p < .001, Cohen’s d = −1.18) and CPPS (mean difference of 0.87, 95% CI [0.20, 1.53], p = .011, Cohen’s d = 1.26). Recordings with Samsung products showed only significant differences and a large effect size with CRS in jitter (mean difference of −0.16, 95% CI [−0.29, −0.03], p = .019, Cohen’s d = −0.84).Conclusions: The present meta-analysis indicated some inconsistency in the outcomes of acoustic voice quality parameters between smartphone recordings and CRS. While acoustic measurements are frequently used in clinical voice assessments and smartphones are widely available, it is important to note that for certain parameters, current smartphone recordings may not yet match the precision of CRSs for voice quality analyses.Supplemental Material S1. Study quality appraisal analyzed with NHLBI Quality Assessment Tool for Observational Cohort and Cross‐Sectional Studies.Barsties v. Latoszek, B., Lammertz, C. Z., Awan, S. N., Binkofski, F., Hetjens, S. (2025). The accuracy of smartphone recordings for clinical voice diagnostics in acoustic voice quality assessments: A systematic review and meta‐analysis. American Journal of Speech-Language Pathology, 34(6), 3531–3548. https://doi.org/10.1044/2025_AJSLP‐25‐00140

Contains video data of ASL signs from PopSign v1.0 game category (test set only).

Video data before landmark data as part of the 2023 Google - Isolated Sign Language Recognition Competition https://www.kaggle.com/competitions/asl-signs See related landmark data here: https://www.kaggle.com/competitions/asl-signs/data

Information below mirrored from original source: https://signdata.cc.gatech.edu/view/datasets/popsign_v1_0

PopSign ASL v1.0

95% of deaf children are born to hearing parents. Since many hearing parents do not know sign, these deaf children are at risk for language acquisition delays resulting in cognitive issues. We are making an educational smartphone game PopSign that helps hearing parents practice their signing vocabulary.

Our dataset is the largest collection of isolated sign videos collected using mobile phones. We are using the data to train recognition models for use in smartphone applications, including the PopSign game. PopSign and related educational technology teach hearing parents and deaf children to sign, reducing developmental problems.

From original paper:

PopSign ASL v1.0 collects examples of 250 isolated American Sign Language signs using the selfie camera on Pixel 4A smartphones in a variety of environments. It is the largest isolated sign language dataset publicly available, the first to focus on one-handed signing with smartphones, and one of the few of its size that has been manually reviewed.

Papers

• PopSign v1.0 Paper
• PopSign v1.0 Supplemental Paper