This dataset contain video transcript from a limited number of youtubers who post Their review on iPhone 15, 15 plus , pro and pro max model . These are the videos used for the videos. Video Credits are owned by respective creators.

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The number of smartphone users in the United States was forecast to continuously increase between 2024 and 2029 by in total 17.4 million users (+5.61 percent). After the fifteenth consecutive increasing year, the smartphone user base is estimated to reach 327.54 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 Mexico and Canada.

The number of Apple iPhone unit sales dramatically increased between 2007 and 2023. Indeed, in 2007, when the iPhone was first introduced, Apple shipped around *** million smartphones. By 2023, 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 2023, includes the **, ** Plus, ** Pro and Pro Max. Powered by the A16 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...

IPhones

## Overview

IPhones is a dataset for object detection tasks - it contains IPhones annotations for 1,802 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

In the first 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.

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

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.

This dataset can be used for Sentiment Analysis which contains the tweets about apple products on twitter. This data set has basically 3 headers 1. tweet_text 2.emotion_in_tweet_is_directed_at 3.is_there_an_emotion_directed_at_a_brand_or_product

This synthetic yet realistic dataset offers insights into smartphone features, customer reviews, and sales data. It includes over 90 customer reviews for six popular smartphone models from leading brands such as Apple, Samsung, and Google. The dataset is designed to help understand how various product specifications influence purchasing decisions and overall customer satisfaction. It combines detailed product specifications, customer star ratings, review texts, and verified purchase status with estimated sales figures per model.

Columns

• model_id (Integer): A unique identifier for each distinct phone model.
• brand (String): The manufacturer of the phone (e.g., "Apple", "Samsung", "Google").
• model_name (String): The specific name of the phone model (e.g., "iPhone 15").
• price (Integer): The retail price of the phone in USD.
• screen_size (Float): The diagonal screen size of the phone in inches.
• battery (Integer): The battery capacity of the phone in mAh.
• camera_main (String): The resolution of the phone's main camera (e.g., "48MP").
• ram (Integer): The amount of RAM (Random Access Memory) in GB.
• storage (Integer): The internal storage capacity in GB.
• has_5g (Boolean): Indicates whether the phone model supports 5G connectivity (TRUE/FALSE).
• water_resistant (String): The water resistance rating, if any (e.g., "IP68" or "None").
• units_sold (Integer): An estimated number of units sold for market analysis purposes.
• review_id (Integer): A unique identifier for each customer review.
• user_name (String): A randomly generated name for the reviewer.
• star_rating (Integer): The customer's rating, ranging from 1 (worst) to 5 (best).
• verified_purchase (Boolean): Indicates whether the reviewer's purchase was verified (TRUE/FALSE).
• review_date (Date): The date when the review was submitted, in YYYY-MM-DD format (e.g., "2023-05-10").
• review_text (String): Simulated text of the customer's review, based on features and rating (e.g., "The 48MP camera is amazing!").

Distribution

The dataset is typically provided in a CSV file format. It comprises over 90 customer review records, along with corresponding smartphone product specifications and sales data for 6 distinct phone models. The exact total number of rows or the specific file size in MB/GB is not specified.

Usage

This dataset is ideal for various analytical applications, including: * Feature importance analysis: Determining which smartphone specifications (e.g., battery life, camera quality) most significantly influence customer ratings and purchasing decisions. * Sentiment analysis: Applying Natural Language Processing (NLP) techniques to extract insights and sentiment from customer review texts. * Pricing strategy optimisation: Analysing the correlation between price and customer satisfaction or sales volume. * Market research: Comparing performance and customer perception across different brands (e.g., Apple vs. Samsung vs. Google) and models. * Sales vs. features correlation: Investigating how product features and pricing impact estimated units sold.

Coverage

This dataset has a Global region coverage. It includes data pertaining to six smartphone models from three major brands: Apple (iPhone 14, iPhone 15), Samsung (Galaxy S22, Galaxy S23), and Google (Pixel 7, Pixel 8). The review dates are indicative of data from around 2023. While it includes customer reviews, specific demographic details of the reviewers are not available beyond randomly generated usernames. As a synthetic dataset, it is designed to be realistic for general market analysis.

License

CC0

Who Can Use It

This dataset is suitable for: * Data Analysts and Scientists: For performing regression analysis, sentiment analysis, and predictive modelling. * Marketing Professionals: To understand consumer preferences, optimise product features, and refine marketing strategies. * Product Managers: To inform product development, feature prioritisation, and competitive analysis. * Market Researchers: To study market trends, brand comparisons, and consumer behaviour in the smartphone industry. * Academics and Students: For educational purposes and research projects related to consumer electronics, e-commerce, and data analysis.

Dataset Name Suggestions

• Smartphone Customer Satisfaction Data
• Mobile Phone Market & Reviews Dataset
• Consumer Electronics Feature Analysis
• Smartphone Product Performance
• Mobile Device Sales and Reviews

Attributes

Original Data Source: Smartphone Feature Optimization (Marketing Mix)

A large-scale mobile typing dataset contains 46 755 participants typing sentences in English and 8661 participants in Finnish on their own mobile devices. Participants used various iPhone and Android devices with different operation system versions. The data was collected between 2019 and 2020 by the Computational Behaviour Lab of Aalto University. The user's typing operations and use of Intelligent Text Entry (ITE) methods (Autocorrection and Suggestion Bar) are labelled on a keystroke level. The dataset enables analysis of the effects of the user demographics and the usage and accuracy of ITE methods on typing. The dataset also has a separate table for all ITE corrected and predicted words e.g. for the ITE error analysis.

Code repository: https://github.com/aalto-speech/ite-typing-dataset/

Citation:

Leino, Katri, Markku Laine, Mikko Kurimo, and Antti Oulasvirta. Mobile Typing with Intelligent Text Entry: A Large-Scale Dataset and Results. 2024. https://doi.org/10.21203/rs.3.rs-4654512/v1

This dataset is a sample from the TalkingData AdTracking competition. I kept all the positive examples (where is_attributed == 1), while discarding 99% of the negative samples. The sample has roughly 20% positive examples.

For this competition, your objective was to predict whether a user will download an app after clicking a mobile app advertisement.

File descriptions

train_sample.csv - Sampled data

Data fields

Each row of the training data contains a click record, with the following features.

• ip: ip address of click.
• app: app id for marketing.
• device: device type id of user mobile phone (e.g., iphone 6 plus, iphone 7, huawei mate 7, etc.)
• os: os version id of user mobile phone
• channel: channel id of mobile ad publisher
• click_time: timestamp of click (UTC)
• attributed_time: if user download the app for after clicking an ad, this is the time of the app download
• is_attributed: the target that is to be predicted, indicating the app was downloaded

Note that ip, app, device, os, and channel are encoded.

I'm also including Parquet files with various features for use within the course.

This synthetic but realistic dataset contains 90+ customer reviews for 6 smartphone models (from Apple, Samsung, and Google), along with: - Product specifications (Price, Screen Size, Battery, Camera, RAM, Storage, 5G, Water Resistance) - Customer reviews (Star Ratings, Review Text, Verified Purchase Status) - Sales data (Units Sold per Model)

Potential Use Cases: ✅ Feature importance analysis (Which specs drive ratings?) ✅ Sentiment analysis (NLP on reviews) ✅ Pricing strategy optimization ✅ Market research (Comparing Apple vs. Samsung vs. Google)

Smartphone Customer Satisfaction Survey

Objective: Understand how product features influence purchasing decisions and satisfaction.

Section 1: Demographic & Purchase Behavior

1. Which smartphone brand did you purchase?

   • ☐ Apple
   • ☐ Samsung
   • ☐ Google
   • Maps to brand column.

2. Which model did you purchase?

   • Apple: ☐ iPhone 14 | ☐ iPhone 15
   • Samsung: ☐ Galaxy S22 | ☐ Galaxy S23
   • Google: ☐ Pixel 7 | ☐ Pixel 8
   • Maps to model_name column.

3. Where did you purchase the phone?

   • ☐ Online (e.g., Amazon, Brand Website)
   • ☐ Physical Store
   • Justifies verified_purchase (assumed online = verified).

Section 2: Product Feature Ratings

1. How would you rate the following features? (1 = Poor, 5 = Excellent)

   • Battery Life: ⭐⭐⭐⭐⭐
   • Camera Quality: ⭐⭐⭐⭐⭐
   • Screen Size: ⭐⭐⭐⭐⭐
   • Performance (RAM/Processor): ⭐⭐⭐⭐⭐
   • Aggregates into star_rating (average of these).

2. Which feature is MOST important to you?

   • ☐ Battery Life
   • ☐ Camera Quality
   • ☐ Screen Size
   • ☐ Performance
   • ☐ Price
   • Explains review_text keywords (e.g., "battery" mentions).

Section 3: Price & Satisfaction

1. How do you feel about the price of your phone?

   • ☐ Very Affordable
   • ☐ Fairly Priced
   • ☐ Slightly Expensive
   • ☐ Too Expensive
   • Maps to price vs. star_rating correlation.

2. Would you recommend this phone to others?

   • ☐ Definitely Yes
   • ☐ Probably Yes
   • ☐ Neutral
   • ☐ Probably No
   • ☐ Definitely No
   • Linked to star_rating (5 = Definitely Yes).

Column Details (Metadata)

Column Name (Type) Description "Example"**

model_id (Integer) Unique ID for each phone model 1 (iPhone 14)

brand (String) Manufacturer (Apple, Samsung, Google) "Apple"

model_name (String) Name of the phone model "iPhone 15"

price (Integer) Price in USD 999

screen_size (Float) Screen size in inches 6.1

battery (Integer) Battery capacity in mAh 4000

camera_main (String) Main camera resolution (MP) "48MP"

ram (Integer) RAM in GB 8

storage (Integer) Storage in GB 128

has_5g (Boolean) Whether the phone supports 5G TRUE

water_resistant (String) Water resistance rating (IP68 or None) "IP68"

units_sold (Integer) Estimated units sold (for market analysis) 15000

review_id (Integer) Unique ID for each review 1

user_name (String) Randomly generated reviewer name "John"

star_rating (Integer) Rating from 1 (worst) to 5 (best) 5

verified_purchase (Boolean) Whether the reviewer bought the product TRUE

review_date (Date) Date of the review (YYYY-MM-DD) "2023-05-10"

review_text (String) Simulated review text based on features & rating "The 48MP camera is amazing!"

Suggested Analysis Ideas to inspire data analysis: A. Feature Impact on Ratings Regression: star_rating ~ battery + camera_main + price Key drivers: Does battery life affect ratings more than camera quality?

B. Sentiment Analysis (NLP) Use tidytext (R) or NLTK (Python) to extract most-loved/hated features. Example: r library(tidytext) reviews_tidy <- final_data %>% unnest_tokens(word, review_text)
reviews_tidy %>% count(word, sort = TRUE) %>% filter(n > 5)

C. Brand Comparison Apple vs. Samsung vs. Google: Which brand has higher average ratings? Price sensitivity: Do cheaper phones (e.g., Pixel) get better value ratings?

D. Sales vs. Features Correlation: units_sold ~ price + brand Premium segment analysis: Do iPhones sell more despite higher prices?

The dataset has been updated to a new version (https://zenodo.org/records/14568959).

The expertise dataset has been added.

Abstract: The Behavioral Emotion Recognition Dataset (BERD) was developed as part of the research study titled "Behavioral Research Methodologies for Bodily Emotion Recognition." This dataset comprises motion capture data collected from participants performing emotional body movements under various experimental conditions. It is designed to facilitate the development and evaluation of automatic emotion recognition (AER) systems using bodily movement data. The dataset offers insights into the effects of participant acting expertise, motion capture device types, and emotional stimuli on bodily emotion recognition accuracy.

Key Features:

1. (Devices) Motion Capture Devices:

• Marker-based motion capture (Optitrack system with 18 infrared cameras).
• Pose estimation using RGB videos.
• Kinect motion capture.
• Mobile phone motion capture (iPhone 12 with ARKit).

2. (Stimulus) Emotional Stimulus:

• Word instructions (e.g., "happy," "sad").
• Picture stimuli (Karolinska Directed Emotional Faces dataset).
• Video stimuli (validated emotional film clips).

3. Emotions Recorded:

• Seven categories: happy, sad, surprised, angry, disgusted, fearful, and neutral.

4. Data Format:

• Skeletal data represented as 3D joint coordinates.
• Sampling rate: 30 frames per second.
• File format: CSV.

Potential Applications:

• Developing deep learning models for bodily emotion recognition.
• Studying the impact of data collection conditions on emotion recognition accuracy.

Citation: If you use this dataset in your research, please cite it as follows:

• Cho, Y., Jung, M., Bae, J., & Kim, K. (2024). Behavioral Emotion Recognition Dataset (BERD). Zenodo. DOI: [https://zenodo.org/records/12577086]

What’s Included

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.

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.

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.

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.

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

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.

1. Specifications Data size : 200,000 ID

Population distribution : race distribution: Asians, Caucasians, black people; gender distribution: gender balance; age distribution: from child to the elderly, the young people and the middle aged are the majorities

Collection environment : indoor scenes, outdoor scenes

Collection diversity : various postures, expressions, light condition, scenes, time periods and distances

Collection device : iPhone, android phone, iPad

Collection time : daytime,night

Image Parameter : the video format is .mov or .mp4, the image format is .jpg

Accuracy : the accuracy of actions exceeds 97%

1. About Nexdata Nexdata owns off-the-shelf PB-level Large Language Model(LLM) Data, 1 million hours of Audio Data and 800TB of Annotated Imagery Data. These ready-to-go machine learning (ML) data supports instant delivery, quickly improve the accuracy of AI models. For more details, please visit us at https://www.nexdata.ai/datasets/computervision?source=Datarade

Data abstract: This Zenodo upload contains the ADVIO data for benchmarking and developing visual-inertial odometry methods. The data documentation is available on Github: https://github.com/AaltoVision/ADVIO

Paper abstract: The lack of realistic and open benchmarking datasets for pedestrian visual-inertial odometry has made it hard to pinpoint differences in published methods. Existing datasets either lack a full six degree-of-freedom ground-truth or are limited to small spaces with optical tracking systems. We take advantage of advances in pure inertial navigation, and develop a set of versatile and challenging real-world computer vision benchmark sets for visual-inertial odometry. For this purpose, we have built a test rig equipped with an iPhone, a Google Pixel Android phone, and a Google Tango device. We provide a wide range of raw sensor data that is accessible on almost any modern-day smartphone together with a high-quality ground-truth track. We also compare resulting visual-inertial tracks from Google Tango, ARCore, and Apple ARKit with two recent methods published in academic forums. The data sets cover both indoor and outdoor cases, with stairs, escalators, elevators, office environments, a shopping mall, and metro station.

Attribution: If you use this data set in your own work, please cite this paper:

Santiago Cortés, Arno Solin, Esa Rahtu, and Juho Kannala (2018). ADVIO: An authentic dataset for visual-inertial odometry. In European Conference on Computer Vision (ECCV). Munich, Germany.

ARKitScenes is an RGB-D dataset captured with the widely available Apple LiDAR scanner. Along with the per-frame raw data (Wide Camera RGB, Ultra Wide camera RGB, LiDar scanner depth, IMU) the authors also provide the estimated ARKit camera pose and ARKit scene reconstruction for each iPad Pro sequence. In addition to the raw and processed data from the mobile device, ARKit.

10 People - 3D&2D Living_Face & Anti_Spoofing Data. The collection scenes is indoor scenes. The dataset includes males and females. The age distribution ranges from juvenile to the elderly, the young people and the middle aged are the majorities. The device includes iPhone X, iPhone XR. The data diversity includes various expressions, facial postures, anti-spoofing samples, multiple light conditions, multiple scenes. This data can be used for tasks such as 3D face recognition, 3D Living_Face & Anti_Spoofing.

ChatGPT ChatGPT

Poe

The dataset to be published was generated through exploratory case studies conducted on wrist-worn devices from three vendors: Huawei, Amazfit, and Xiaomi. The specific devices investigated include the Huawei Fit 2 Smartwatch and Band 7, Amazfit Band 7, and Xiaomi Watch 3. These devices operate on different operating systems, namely Android Wear, Zepp OS, and Wear OS.

The data collection period for each device varies, with Huawei having approximately one year of data collected, while the other devices have shorter durations. All wrist-wear devices from different vendors were connected to an iPhone 11 mobile device, which acted as the host device. The iPhone facilitated data synchronization and provided access to the data through the respective health applications provided by the vendors.

To extract the data, MD-NEXT was employed, and the extracted data was further analyzed using the MD-RED tool. These tools were chosen due to their recognized forensically sound capabilities. As a result, the dataset contains data that is considered suitable for use in digital forensics fields.

Overall, the dataset provides valuable information obtained from wrist-worn devices, covering multiple vendors, operating systems, and data collection periods. Researchers in the digital forensics field can utilize this dataset for various investigative and analytical purposes.