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 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 contains reviews of the iPhone 14 from various customers. Each entry in the dataset includes information such as the title of the review, the rating given by the customer, the detailed review text, the name of the customer, the date when the review was posted, and the location of the customer. These reviews offer insights into customer opinions, satisfaction levels, and preferences regarding the iPhone 14.

Column Descriptions: 1. Title: Title or headline of the review provided by the customer. It summarizes the main point or sentiment expressed in the review. 2. Rating: Numerical rating given by the customer for the iPhone 14. Ratings typically range from 1 to 5, with 5 indicating the highest satisfaction level. 3. Review: Detailed text of the review where the customer shares their experiences, opinions, and feedback about the iPhone 14. 4. Customer Name: Name or identifier of the customer who wrote the review. It helps in tracking individual reviewers and analyzing their feedback. 5. Dates: Date when the review was posted by the customer. It provides temporal information for analyzing trends and changes in customer sentiment over time. 6. Customer Location: Location of the customer who wrote the review. It can be useful for demographic analysis, understanding regional preferences, and market segmentation.

This dataset can be used for sentiment analysis, feature analysis, trend analysis, customer profiling, and market research related to the iPhone 14.

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 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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For more datasets, click here.

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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...

This dataset has been artificially generated to mimic real-world user interactions within a mobile application. It contains 100,000 rows of data, each row of which represents a single event or action performed by a synthetic user. The dataset was designed to capture many of the attributes commonly tracked by app analytics platforms, such as device details, network information, user demographics, session data, and event-level interactions.

Key Features Included

User & Session Metadata

User ID: A unique integer identifier for each synthetic user. Session ID: Randomly generated session identifiers (e.g., S-123456), capturing the concept of user sessions. IP Address: Fake IP addresses generated via Faker to simulate different network origins. Timestamp: Randomized timestamps (within the last 30 days) indicating when each interaction occurred. Session Duration: An approximate measure (in seconds) of how long a user remained active. Device & Technical Details

Device OS & OS Version: Simulated operating systems (Android/iOS) with plausible version numbers. Device Model: Common phone models (e.g., “Samsung Galaxy S22,” “iPhone 14 Pro,” etc.). Screen Resolution: Typical screen resolutions found in smartphones (e.g., “1080x1920”). Network Type: Indicates whether the user was on Wi-Fi, 5G, 4G, or 3G. Location & Locale

Location Country & City: Random global locations generated using Faker. App Language: Represents the user’s app language setting (e.g., “en,” “es,” “fr,” etc.). User Properties

Battery Level: The phone’s battery level as a percentage (0–100). Memory Usage (MB): Approximate memory consumption at the time of the event. Subscription Status: Boolean flag indicating if the user is subscribed to a premium service. User Age: Random integer ranging from teenagers to seniors (13–80). Phone Number: Fake phone numbers generated via Faker. Push Enabled: Boolean flag indicating if the user has push notifications turned on. Event-Level Interactions

Event Type: The action taken by the user (e.g., “click,” “view,” “scroll,” “like,” “share,” etc.). Event Target: The UI element or screen component interacted with (e.g., “home_page_banner,” “search_bar,” “notification_popup”). Event Value: A numeric field indicating additional context for the event (e.g., intensity, count, rating). App Version: Simulated version identifier for the mobile application (e.g., “4.2.8”). Data Quality & “Noise” To better approximate real-world data, 1% of all fields have been intentionally “corrupted” or altered:

Typos and Misspellings: Random single-character edits, e.g., “Andro1d” instead of “Android.” Missing Values: Some cells might be blank (None) to reflect dropped or unrecorded data. Random String Injections: Occasional random alphanumeric strings inserted where they don’t belong. These intentional discrepancies can help data scientists practice data cleaning, outlier detection, and data wrangling techniques.

Usage & Applications

Data Cleaning & Preprocessing: Ideal for practicing how to handle missing values, inconsistent data, and noise in a realistic scenario. Analytics & Visualization: Demonstrate user interaction funnels, session durations, usage by device/OS, etc. Machine Learning & Modeling: Suitable for building classification or clustering models (e.g., user segmentation, event classification). Simulation for Feature Engineering: Experiment with deriving new features (e.g., session frequency, average battery drain, etc.).

Important Notes & Disclaimer

Synthetic Data: All entries (users, device info, IPs, phone numbers, etc.) are artificially generated and do not correspond to real individuals. Privacy & Compliance: Since no real personal data is present, there are no direct privacy concerns. However, always handle synthetic data ethically.

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

Introducing the English Product Image Dataset - a diverse and comprehensive collection of images meticulously curated to propel the advancement of text recognition and optical character recognition (OCR) models designed specifically for the English language.

Dataset Contain & Diversity

Containing a total of 2000 images, this English OCR dataset offers diverse distribution across different types of front images of Products. In this dataset, you'll find a variety of text that includes product names, taglines, logos, company names, addresses, product content, etc. Images in this dataset showcase distinct fonts, writing formats, colors, designs, and layouts.

To ensure the diversity of the dataset and to build a robust text recognition model we allow limited (less than five) unique images from a single resource. Stringent measures have been taken to exclude any personally identifiable information (PII) and to ensure that in each image a minimum of 80% of space contains visible English text.

Images have been captured under varying lighting conditions – both day and night – along with different capture angles and backgrounds, to build a balanced OCR dataset. The collection features images in portrait and landscape modes.

All these images were captured by native English people to ensure the text quality, avoid toxic content and PII text. We used the latest iOS and Android mobile devices above 5MP cameras to click all these images to maintain the image quality. In this training dataset images are available in both JPEG and HEIC formats.

Metadata

Along with the image data, you will also receive detailed structured metadata in CSV format. For each image, it includes metadata like image orientation, county, language, and device information. Each image is properly renamed corresponding to the metadata.

The metadata serves as a valuable tool for understanding and characterizing the data, facilitating informed decision-making in the development of English text recognition models.

Update & Custom Collection

We're committed to expanding this dataset by continuously adding more images with the assistance of our native English crowd community.

If you require a custom product image OCR dataset tailored to your guidelines or specific device distribution, feel free to contact us. We're equipped to curate specialized data to meet your unique needs.

Furthermore, we can annotate or label the images with bounding box or transcribe the text in the image to align with your specific project requirements using our crowd community.

License

This Image dataset, created by FutureBeeAI, is now available for commercial use.

Conclusion:

Leverage the power of this product image OCR dataset to elevate the training and performance of text recognition, text detection, and optical character recognition models within the realm of the English language. Your journey to enhanced language understanding and processing starts here.

Infant Crying smartphone speech dataset, collected by Android smartphone and iPhone, covering infant crying. Our dataset was collected from extensive and diversify speakers(201 people in total, with balanced gender distribution), geographicly speaking, enhancing model performance in real and complex tasks. Quality tested by various AI companies. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.

The release of the first iPhone was On June 29, 2007 so this dataset provide the historical data of APPl stock

This dataset contains historical data for appl stock. The dataset provides information for each trading day, including the date, price, open, high, low, volume, and percentage change.

Here is a description of the columns in the dataset :

1) Date: (datetime64) This column represents the date of the trading day. It indicates when the data was recorded.

2) Price: (float64) The price column represents the index's closing price on each trading day. It shows the value at which the index concluded its trading session.

3) Open: (float64) This column denotes the opening price of the index on each trading day. It represents the value at which the index began trading at the start of the session.

4) High: (float64) The high column indicates the highest price reached by the index during the trading day. It represents the peak value recorded for the index's price.

5) Low: (float64) The low column represents the lowest price reached by the index during the trading day. It indicates the minimum value recorded for the index's price.

6) Vol.: (object) The volume column denotes the trading volume, usually measured in millions, for each trading day. It represents the total number of shares or contracts traded during the session.

7) Change %: (float64) This column provides the percentage change in the index's price from the previous trading day. It indicates the daily price movement of the index.

The dataset contains 4034 rows, including the header row that describes the columns. The actual data starts from the second row and provides information for each trading day in descending order, with the most recent date appearing first.

The dataset created at the 2024 National Festival in Portugal consists of 9062 images. It is a dataset designed for recognizing objects from the YCB dataset along with detecting people. It comprises 1500 images of people from the COCO dataset, 416 images taken in the arena with the Astra camera of the TIAGo robot, 2146 images taken with an iPhone, and the rest of the images are from the @Home Objects Dataset.

Japanese(Japan) Full-Duplex Spontaneous Dialogue Smartphone speech dataset, collected from dialogues based on given topics. Transcribed with text content, speaker's ID, gender, age and other attributes. Our dataset was collected from extensive and diversify speakers, geographicly speaking, enhancing model performance in real and complex tasks.

Format 24kHz, 16 bit, wav, mono channel;

Content category Free dialogue based on given topics

Recording condition Low background noise (indoor);

Recording device Android smartphone, iPhone;

Speaker About 200 people;

Country Japan(JPN);

Language Japanese;

Features of annotation Transcription text, timestamp, speaker ID, gender.

Accuracy Rate Sentence Accuracy Rate (SAR) 95%(the error rate of noise symbols and other identifiers is not included, because the labeling of non-speech events is more subjective)

https://1000logos.net/wp-content/uploads/2018/09/35-Best-Cell-Phone-Company-Brands-and-logos.png" alt="Cell Phone Company Brands">

Hey there, phone fans! This dataset dives into the hottest-selling mobile phones, giving you the lowdown on the Top 120 models that ruled the market. We've got data on:

Manufacturer: The big names behind the phones, like Apple, Samsung, and whoever else is cooking up the latest tech (you know, these Tony Starks). Model: The specific phone name, because let's face it, not all iPhones are created equal (looking at you, iPhone 3G). Form Factor: We're talking about the phone's overall shape and style, whether it's a classic Bar, a sleek Touchscreen, or Keyboard bar phone. Year: The year these phones hit the shelves, so you can see how trends changed over time. Units Sold (Million): This is the big one - how many millions of people actually bought these phones?

So, if you're curious about the cell phone hall of fame, or just want to see how phone technology has evolved over the years, this dataset is your one-stop shop.

Italian(Italy) Scripted Monologue Smartphone speech dataset, collected from monologue based on given prompts, covering oral; human-machine interaction; smart home command and in-car command; numbers; news domains. Transcribed with text content. Our dataset was collected from extensive and diversify speakers(3,109 native speakers), geographicly speaking, enhancing model performance in real and complex tasks. Quality tested by various AI companies. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.

Format

16kHz, 16bit, uncompressed wav, mono channel

Content category

oral category; human-machine interaction category; smart home command and in-car command category; numbers; news category

Recording condition

Low background noise (indoor)

Recording device

Android smartphone, iPhone

Country

Italy(ITA)

Language(Region) Code

it-IT

Language

Italian

Speaker

3,109 people from Italy, 48% male and 52% female

Features of annotation

Transcription text

Device

Android mobile phone, iPhone

Accuracy rate

Word Accuracy Rate(WAR) 95%

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).

Noise pollution in cities has major negative effects on the health of both humans and wildlife. Using iPhones, we collected sound-level data at hundreds of locations in four areas of Boston, Massachusetts (USA) before, during, and after the fall 2020 pandemic lockdown, during which most people were required to remain at home. These spatially dispersed measurements allowed us to make detailed maps of noise pollution that are not possible when using standard fixed sound equipment. The four sites were: the Boston University campus (which sits between two highways), the Fenway/Longwood area (which includes an urban park and several hospitals), Harvard Square (home of Harvard University), and East Boston (a residential area near Logan Airport). Across all four sites, sound levels averaged 6.4 dB lower during the pandemic lockdown than after. Fewer high noise measurements occurred during lockdown as well. The resulting sound maps highlight noisy locations such as traffic intersections and qui..., We collected sound measurements within four different urban sites in Boston, Massachusetts. Working in small teams of 2-4 people, we used the mobile app SPLnFFT to collect sound level data in A-weighted decibel readings using smartphones. We exclusively used iPhones for data collection for consistency in hardware and software. Before each collection, we calibrated each iPhone to the same standard, which was used for every collection outing. We recorded the L50 value (the median sound level) for each recording because the L50 value is less affected by short bursts of loud sound than the mean reading. Recordings ran for approximately 20 seconds each. We recorded all sound measurements between 9 am and 5 pm on workdays to avoid the influence of rush-hour traffic, and only collected data on days without rain, snow, or strong wind to prevent inaccuracies due to weather. Within these conditions, we collected sound measurements over multiple days and at different times to ensure representative..., , # Data from: Maps made with smartphones highlight lower noise pollution during COVID-19 pandemic lockdown at four locations in Boston

https://doi.org/10.5061/dryad.ncjsxkt35

Dataset contents include csv files of all data (each file describes collection year and site of data), R script used to create noise maps, and kml files needed to run the map creation code.

Description of the data and file structure

Each csv file contains the L50 values (median sound level) taken from hundreds of 20 second recordings over multiple collection days. The SPLnFFT application exports the latitude and longitude of where the recording was taken, which is also included in the csv files and is used to create the noise maps. The csv files are used as data frames for the R script to create noise maps for each collection site. The R script contains comments and instructions to clearly indicate each step of the map creation. The kml files are used to create bound...

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.

English(France) Scripted Monologue Smartphone speech dataset, collected from monologue based on given scripts, covering generic domain, human-machine interaction, smart home command and in-car command, numbers and other domains. Transcribed with text content and other attributes. Our dataset was collected from extensive and diversify speakers(1,089 people in total), geographicly speaking, enhancing model performance in real and complex tasks. Quality tested by various AI companies. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.

Format

16kHz, 16bit, uncompressed wav, mono channel;

Recording condition

Low background noise(indoor), without echo;

Content category

Generic domain; human-machine interaction; smart home command and in-car command; numbers;

Recording device

Android Smartphone, iPhone;

Speaker

1,089 speakers totally, with 49% male and 51% female; and 61% speakers of all are in the age group of 18-25, 34% speakers of all are in the age group of 26-45, 5% speakers of all are in the age group of 46-60;

Country

France(FRA);

Language

English;

Features of annotation

Transcription text;

Accuracy Rate

Sentence Accuracy Rate (SAR) 95%

The number of mobile broadband connections in the Philippines was forecast to continuously increase between 2024 and 2029 by in total 18.3 million connections (+20.46 percent). After the ninth consecutive increasing year, the number of connections is estimated to reach 107.69 million connections and therefore a new peak in 2029. Mobile broadband connections include cellular connections with a download speed of at least 256 kbit/s (without satellite or fixed-wireless connections). Cellular Internet-of-Things (IoT) or machine-to-machine (M2M) connections are excluded. 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 mobile broadband connections in countries like Vietnam and Laos.