The global smartphone penetration in was forecast to continuously increase between 2024 and 2029 by in total 20.3 percentage points. After the fifteenth consecutive increasing year, the penetration is estimated to reach 74.98 percent and therefore a new peak in 2029. Notably, the smartphone penetration of was continuously increasing over the past years.The penetration rate refers to the share of the total population.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 smartphone penetration in countries like North America and the Americas.

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.

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.

Description for each of the variables:

1. Brands: The brands of smartphones included in the dataset.
2. Colors: The colors available for the smartphones.
3. Memory: The storage capacity of the smartphones, typically measured in gigabytes (GB) or megabytes (MB).
4. Storage: The internal storage capacity of the smartphones, often measured in gigabytes (GB) or megabytes (MB).
5. Rating: The user ratings or scores assigned to the smartphones, reflecting user satisfaction or performance.
6. Selling Price: The price at which the smartphones are sold to consumers.
7. Original Price: The original or list price of the smartphones before any discounts or promotions.
8. Mobile: Indicates whether the device is a mobile phone.
9. Discount: The discount applied to the original price to calculate the selling price.
10. Discount percentage: The percentage discount applied to the original price to calculate the selling price.

Percentage of Canadians using a smartphone for personal use and selected habits of use during a typical day.

We will create a customized phones dataset tailored to your specific requirements. Data points may include brand names, model specifications, pricing information, release dates, market availability, feature sets, and other relevant metrics.

Utilize our phones datasets for a variety of applications to boost strategic planning and market analysis. Analyzing these datasets can help organizations grasp consumer preferences and technological trends within the mobile phone industry, allowing for more precise product development and marketing strategies. You can choose to access the complete dataset or a customized subset based on your business needs.

Popular use cases include: enhancing competitive benchmarking, identifying pricing trends, and optimizing product portfolios.

The global number of smartphone users in was forecast to continuously increase between 2024 and 2029 by in total 1.8 billion users (+42.62 percent). After the ninth consecutive increasing year, the smartphone user base is estimated to reach 6.1 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 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 Australia & Oceania and Asia.

About Dataset

This dataset contains information about mobile phones available in Ghana, including details about various phone models, their specifications, and pricing. The data was collected through web scraping, providing a comprehensive overview of the mobile phone market in Ghana.

Key Features

• Brand & Model: The dataset includes details on various phone models from different brands, allowing users to explore a wide range of options.

• Specifications: Detailed phone specifications are provided, such as whether the phone supports an SD card, the main camera setup, resolution, display type, SIM card configuration, operating system, color options, and more.

• Geographical Information: Users can filter and analyze the dataset based on region and location in Ghana, making it useful for understanding the availability of different phone models in specific areas.

• Hardware & Software: Essential hardware features like screen size (in inches), battery capacity (in mAh), storage (in GB), RAM (in GB), and selfie camera resolution (in MP) are included.

• Pricing: The dataset also provides pricing information (in Ghanaian Cedis - ¢), enabling users to compare the cost of various phone models.

This dataset is valuable for consumers, researchers, and businesses interested in the mobile phone market in Ghana. It can be used for market analysis, consumer insights, and decision-making related to mobile phone purchases. Researchers can also use the data for further analysis and modeling.

This dataset provides comprehensive information about various Samsung smartphones, including their dimensions, system-on-chip (SoC), central processing unit (CPU), graphics processing unit (GPU), RAM, storage capacity, display specifications, battery details, operating system (OS), and camera attributes. Each row represents a different Samsung smartphone model, and the dataset contains valuable data for comparative analysis, research, or exploring the features of these smartphones. With details on multiple key specifications, this dataset is a valuable resource for tech enthusiasts, consumers, and analysts interested in Samsung's mobile offerings.

The dataset offers a structured format for easily comparing and contrasting different Samsung smartphone models, making it a valuable tool for decision-making, market analysis, and understanding the evolving landscape of Samsung's mobile devices.

Dataset Overview: A collection of features characterizing mobile phones, including battery power, camera specifications, network support, memory, screen dimensions, and other attributes. The 'price_range' column categorizes phones into price ranges, making this dataset suitable for mobile phone classification and price prediction tasks.

We present the dataset for the article "Keep on scrolling? Using intensive longitudinal smartphone sensing data to assess how everyday smartphone usage behaviors are related to well-being". The data were collected as part of the Smartphone Sensing Panel Study and comprise several dataset parts, as we replicated our analysis for two different 14-day measurement periods (A and B). At the macro level, we aggregated different measures of smartphone use (measured by mobile sensing) over 14 days and examined their associations with global survey-based measures of well-being (Flourishing, Satisfaction WIth Life, Positive Activation, Negative Activation, Valence; Dataset A: N = 236, Dataset B: N = 305). At the micro level, we aggregated various measures of smartphone use (measured via mobile sensing) over 60-minute windows before asking participants about their current mood using experience sampling questionnaires (Dataset A: N = 378, n = 5775; Dataset B: N = 534, n = 7287). In our supplementary analysis, we also aggregated the smartphone usage data for 15-minute windows to analyse social and non-social situations. Demographic variables (age, gender, education) that were not used for the data analyses were removed for privacy reasons, but can be provided upon request. The datasets are documented by a comprehensive accompanying codebook. Additional materials (e.g., preprocessing and analysis code) can also be found at https://osf.io/ckwge/ Further details on the variables provided and the associated study procedures can be found in the journal article: große Deters, F., & Schoedel, R. (2024). Keep on scrolling? Using intensive longitudinal smartphone sensing data to assess how everyday smartphone usage behaviors are related to well-being, Computers in Human Behavior, 150, 107977, https://doi.org/10.1016/j.chb.2023.107977

Model:

Description: The name of the smartphone model. Example: "Samsung Galaxy S21", "iPhone 13", "Google Pixel 6". Notes: This is a categorical variable that uniquely identifies each phone. Price:

Description: The cost of the smartphone, typically in the local currency (e.g., USD). Example: 999, 799, 699. Notes: This is a numerical variable, which can be used to analyze the affordability and market positioning of different models. RAM:

Description: The amount of random-access memory (RAM) in the smartphone, typically measured in gigabytes (GB). Example: 4 GB, 8 GB, 12 GB. Notes: This numerical variable impacts the phone's ability to handle multiple tasks simultaneously and affects overall performance. Display:

Description: The specifications of the smartphone's display, often given in terms of size (in inches) and resolution. Example: "6.1 inches, 1080x2400 pixels". Notes: This variable is usually a mix of numerical and categorical data, reflecting the screen size and resolution. Rear Camera:

Description: The specifications of the main (rear) camera(s), often including the number of cameras, megapixels (MP), and other features (e.g., wide-angle, telephoto). Example: "12 MP + 12 MP dual", "108 MP". Notes: This is often a categorical variable with numerical components, indicating the camera's capabilities. Front Camera:

Description: The specifications of the front (selfie) camera, typically measured in megapixels. Example: "10 MP", "32 MP". Notes: Similar to the rear camera, this is a categorical variable with numerical components, indicating the quality of the front camera. Battery:

Description: The battery capacity of the smartphone, typically measured in milliampere-hours (mAh). Example: 4000 mAh, 5000 mAh. Notes: This numerical variable impacts the phone's battery life and usage duration. Processor:

Description: The type and model of the smartphone's processor (CPU). Example: "Snapdragon 888", "Apple A14 Bionic". Notes: This categorical variable indicates the processing power and efficiency of the phone. Star Ratings:

Description: The average user rating of the smartphone, typically on a scale from 1 to 5 stars. Example: 4.5, 3.8. Notes: This numerical variable reflects user satisfaction and can be used to gauge the overall reception of the phone. Ratings:

Description: The total number of user ratings received for the smartphone. Example: 1500, 5000. Notes: This numerical variable indicates the popularity and extent of user feedback.

Mobile Phone Dataset | Smartphone & Feature Phone

Crowdsourced original images of a wide variety of mobile phones

About Dataset

This dataset is collected by* DataCluster Labs*, India. To download full dataset or to submit a request for your new data collection needs, please drop a mail to: sales@datacluster.ai

This dataset is an extremely challenging set of over 3000+ original Mobile Phone images captured and crowdsourced from over 1000+ urban and rural areas, where each image is manually reviewed and verified by computer vision professionals at ****DC Labs.

Dataset Features 1. Dataset size : 3000+ 2. Captured by : Over 1000+ crowdsource contributors 3. Resolution : 99% images HD and above (1920x1080 and above) 4. Location : Captured with 600+ cities accross India 5. Diversity : Various lighting conditions like day, night, varied distances, view points etc. 6. Device used : Captured using mobile phones in 2020-2021 7. Applications : Mobile Phone detection, cracked screen detection, etc.

Available Annotation formats COCO, YOLO, PASCAL-VOC, Tf-Record

The images in this dataset are exclusively owned by Data Cluster Labs and were not downloaded from the internet. To access a larger portion of the training dataset for research and commercial purposes, a license can be purchased. Contact us at sales@datacluster.ai

Visit www.datacluster.ai to know more.

Sector: 01. Ending all forms of poverty in the world

Algorithm: Males aged 6 years and over who use their mobile phones every day out of the total number of males aged 6 years and over * 100

Phenomenon: Stock

The energy consumption of Android devices, measured via data collection from features, is a recurring theme in the literature. To evaluate the performance of such devices, databases are generated by collecting data from features while using the Android operating system. This is a database generated using Tucandeira Data Collector from the daily use of smartphones and tablets while performing everyday tasks. The dataset contains 98 features and 10,331,114 records related to dynamic, background, list of applications, and static data. Device records were collected daily from ten distinct devices and stored in CSV files that were later organized to generate a database by cleaning and preprocessing the data that are publically available in the Mendeley Data Repository. The dataset formed an integral component of the SWPERFI RD&I Project, a research, development, and innovation initiative aimed at improving the performance and energy optimization of mobile devices. This project was undertaken at the Federal University of Amazonas.

This dataset contains information on over 13,000 different mobile phones scraped from 91 Mobiles, the largest gadget discovery site in India.

However, the dataset is uncleaned and contains a number of data quality issues, particularly in the last two columns (Per_Dis_Cam_Bat and Other_Features).

This makes it a great resource for those looking to practice data assessment and cleaning techniques. After cleaning and tidying the data, it can be used for exploratory data analysis (EDA), market analysis, and other research purposes related to the Indian mobile phone market.

The dataset includes the following columns:

Name: The name of the phone, including the brand and model. Spec Score: A rating given by 91 Mobiles based on various specifications and features. Operating System: The operating system the phone comes with out of the box. Price: The cost of the phone in Indian rupees. Per_Dis_Cam_Bat: This column includes data on the phone's processor, display, battery, and camera. Other Features: Additional details like the phone's storage capacity, SIM card type, fingerprint sensor, etc.

The average time spent daily on a phone, not counting talking on the phone, has increased in recent years, reaching a total of * hours and ** minutes as of April 2022. This figure was expected to reach around * hours and ** minutes by 2024.

Irys specializes in delivering high-quality Location Data solutions for worldwide locations. Our unique data sourcing approach ensures superior data quality and volume. Highlight key features for target use cases in Mobility Data, Places Data, Footfall Data, and Foot Traffic Data.

Data Attributes:

• Raw GPS Data
• Hourly Cadence

Method: Real-Time

Experiment with various pricing strategies, offering transparency and flexibility to meet diverse needs.

Our commitment to privacy compliance is unwavering. All data is collected transparently with clear privacy notices. Opt-in/out management empowers users over data distribution. Customer testimonials speak to our reliability.

Experience the precision of our Location Data solutions—where quality meets privacy compliance.

1) Data Introduction • The Phones price classification dataset is a collection of mobile phone sales data from various companies to estimate the price of a mobile phone.

2) Data Utilization (1) Phones price classification data has characteristics that: • The dataset includes factors related to the performance of the mobile phone such as battery power, speed, dual sim and internal memory. (2) Phones price classification data can be used to: • Market Research: Help you understand competitors' product features and pricing strategies, and develop differentiation strategies. • Customer Preference Analysis: Identify the features of your mobile phone that you value.