This dataset contains detailed specifications and official launch prices of various mobile phone models from different companies. It provides insights into smartphone hardware, pricing trends, and brand competitiveness across multiple countries. The dataset includes key features such as RAM, camera specifications, battery capacity, processor details, and screen size.

One important aspect of this dataset is the pricing information. The recorded prices represent the official launch prices of the mobile phones at the time they were first introduced in the market. Prices vary based on the country and the launch period, meaning older models reflect their original launch prices, while newer models include their most recent launch prices. This makes the dataset valuable for studying price trends over time and comparing smartphone affordability across different regions.

Features:

• Company Name: The brand or manufacturer of the mobile phone.
• Model Name: The specific model of the smartphone.
• Mobile Weight: The weight of the mobile phone (in grams).
• RAM: The amount of Random Access Memory (RAM) in the device (in GB).
• Front Camera: The resolution of the front (selfie) camera (in MP).
• Back Camera: The resolution of the primary rear camera (in MP).
• Processor: The chipset or processor used in the device.
• Battery Capacity: The battery size of the smartphone (in mAh).
• Screen Size: The display size of the smartphone (in inches).
• Launched Price: (Pakistan, India, China, USA, Dubai): The official launch price of the mobile in the respective country at the time of its release. Prices vary based on the year the mobile was launched.
• Launched Year: The year the mobile phone was officially launched.

About Dataset

This dataset contains detailed information about phones listed on Amazon, including product specifications, user reviews, ratings, and pricing. The dataset can be useful for analyzing product trends, consumer preferences, pricing strategies, and technical features of smartphones sold on the platform. It includes both new and Amazon-renewed phones.

Description

The dataset includes the following key features:

• Color: The available color of the phone.
• Image Links: URLs to the images of the products.
• Descriptions: Detailed descriptions of the phone, including specifications.
• Kind Product: The type or category of the product (smartphones, accessories, etc.).
• Ratings: User ratings (out of 5 stars).
• Number of Ratings: Total count of ratings the product has received.
• Status: Availability status (e.g., In Stock, Out of Stock).
• Number of Buyers Last Month More Than: Approximate number of buyers in the previous month.
• Typical Price: The regular price with usd of the phone without any discounts.
• Price: The current price with usd of the phone.
• You Save: The amount saved if the phone is on discount.
• Discount: The percentage discount offered on the product.
• Brand: The brand name of the phone (e.g., Apple, Samsung).
• OS: The operating system of the phone (e.g., Android, iOS).
• CPU Model: The model of the processor used in the phone.
• Resolution: The screen resolution of the phone.
• Name: The product name as listed on Amazon.
• Wireless Carrier: The supported wireless carrier (e.g., Verizon, AT&T).
• Cellular Technology: The cellular network technology (e.g., 4G, 5G).
• Dimensions: Physical dimensions of the phone.
• ASIN: Amazon Standard Identification Number, a unique product identifier.
• Model: The model number of the phone.
• Amazon Renewed: Indicates whether the product is part of the Amazon Renewed program (refurbished).
• Renewed Smartphones: Additional flag indicating if the phone is renewed.
• Battery Capacity: The capacity of the phone’s battery (in mAh).
• Battery Power: The power rating of the battery.
• Charging Time: Time taken to charge the phone fully.
• RAM: The amount of RAM in the phone.
• Storage: Internal storage capacity of the phone.
• Screen Size: Size of the display (in inches).
• Connectivity Technologies: Wireless technologies supported by the phone (e.g., Bluetooth, Wi-Fi).
• Wireless Network: Type of wireless networks supported (e.g., Wi-Fi 6).
• CPU Speed: The speed of the phone’s CPU (in GHz).
• Reviews USA: User reviews originating from the USA.
• Reviews Other: User reviews from countries other than the USA.

Detail

This dataset includes a comprehensive range of variables, offering insight into both the technical aspects and customer perceptions of various smartphones sold on Amazon. The dataset allows for:

• Product Comparisons: Comparison of specifications like RAM, CPU, storage, battery life, screen size, etc.
• Pricing Analysis: Understanding pricing trends, discounts, and price fluctuations across different brands and models.
• Consumer Insights: Analysis of consumer behavior through ratings, reviews, and the number of buyers over time.
• Product Availability: Insights into stock availability and how often certain products are sold or renewed.

Usage

The dataset can be used for several purposes, including but not limited to:

1. Market Research: Analyze product popularity and trends in smartphone sales on Amazon.
2. Sentiment Analysis: Perform sentiment analysis on reviews (USA and other countries) to understand customer satisfaction.
3. Price Forecasting: Build models to forecast price changes or identify the best time to buy based on historical data.
4. Product Recommendations: Develop recommendation systems based on user reviews, ratings, and product features.
5. Competitive Analysis: Compare different brands and models to identify strengths and weaknesses of various smartphones.
6. Feature Engineering for ML Models: Use product specifications like RAM, CPU speed, and battery power to create features for predictive machine learning models.

Summary

This Amazon product phones dataset provides an in-depth look at smartphones sold on Amazon, covering everything from technical specifications to user reviews and pricing. It is ideal for anyone looking to analyze trends in the smartphone market, consumer preferences, or technical specifications. The data can be leveraged for a wide array of projects such as market analysis, machine learning, and competitive intelligence.

Imagine waking up to the gentle buzz of your phone, checking the morning news, scrolling through messages, and booking your ride to work, all before even leaving your bed. This small routine speaks volumes about the place mobile phones hold in our lives today. By 2025, mobile phones aren’t just...

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.

Do upvote if you found this dataset helpful 😊

Overview

This dataset contains detailed specifications and ratings for 5000+ mobile phones, scraped from MySmartPrice. It includes information about smartphone models, prices, ratings, hardware specifications, and additional features.

Data Summary

• Total Rows: 5,263
• Total Columns: 15
• Data Source: MySmartPrice (Scraped)
• Last Updated: [February 2025]

Features

• mobile_name – Brand name and model name.
• release_date – Official release date.
• price – Price of the mobile phone in INR (₹).
• avg_rating – Average user rating (out of 5).
• total_ratings – Total number of user ratings.
• cpu – Processor details (e.g., Snapdragon 8 Gen 3, Apple A17 Bionic).
• rear_camera – Rear camera specifications (e.g., 50MP + 12MP + 10MP).
• front_camera – Front camera details.
• display – Screen size, refresh rate, and display type.
• ram_and_storage – RAM and internal storage configurations.
• battery_and_charging_speed – Battery capacity (mAh) and charging speed (W).
• operating_system – Pre-installed OS version (e.g., Android v14, iOS 17).
• 5G|NFC|Fingerprint – Indicates the presence of 5G, NFC, and fingerprint sensors.
• expert_view – Expert opinions or summary reviews from MySmartPrice.

Usage

This dataset is useful for: - Analyzing smartphone market trends. - Building price prediction models. - Comparing smartphone features. - Sentiment analysis of ratings.

Notes

• Some fields may contain missing values due to data unavailability.
• Prices may fluctuate over time.
• Ratings data may be incomplete for newly released models.

In 2021, the number of mobile devices operating worldwide stood at almost 15 billion, up from just over 14 billion in the previous year. The number of mobile devices is expected to reach 18.22 billion by 2025, an increase of 4.2 billion devices compared to 2020 levels.

Moving forward with 5G

As the number of devices grows, so does our dependence on them to fulfill daily functions and activities. The use cases for mobile devices increasingly demand faster connection speeds and lower latency. The 5G network will be critical to fulfilling those demands, operating at significantly faster rates than 4G. In North America, for example, it is expected that there will be 218 million 5G connections, up from just ten million in 2020. This means around 48 percent of all mobile connections in North America. Globally, this figure should reach 20.1 percent by 2025.

6G: looking beyond 5G

While 5G has entered commercialization and is already creating new opportunities, researchers and engineers are already experimenting with 6G. Not only will the number of mobile devices continue to grow but cellular internet-of-things (IoT) devices are set to permeate more industrial sectors in the coming years, meaning a solution will eventually be required for network congestion and data transfer speeds.

6G ought to be capable of solving those problems before they arise, potentially enabling a network connection density ten times greater than that of 5G, and peak data rates up to fifty times faster than the rate of 5G. The Federal Communications Commission in the United States has opened spectrum for experimentation, and China have already launched what is described as a 6G satellite, so that actual potential of 6G should be revealed over the coming decade.

Description: This dataset was generated using a Python web scraper to extract detailed mobile phone listings from Skroutz, a popular Greek e-commerce website. The dataset includes a wide range of information on various mobile phones, making it a valuable resource for analyzing market trends and consumer preferences in the mobile phone industry. Additionally, the dataset has been thoroughly cleaned to ensure accuracy and usability. Below is a breakdown of the columns included in the dataset:

• Title: The name and description of the phone.
• Price: The price of the phone in Euros (€).
• Model: The year the phone model was released.
• Screen: The type of screen technology used (e.g., OLED, Super AMOLED).
• Reviews: The total number of reviews the phone has received.
• Shop_Stock: The number of shops where the phone is available.
• Color: The color of the phone.
• Screen_size: The size of the phone's screen in inches.
• Battery/mAh: The capacity of the phone's battery in milliampere-hours (mAh).
• Star_Rating: The average star rating of the phone.
• phone_type: The type of phone (e.g., Smartphone).
• Has_Reviews: A boolean indicating whether the phone has reviews.
• Brand: Phone brands (e.g., Xiaomi, Apple).

This dataset provides an overview of mobile phone specifications and availability, making it suitable for various analyses such as price comparison, feature trends, and consumer preferences.

BackgroundThis study aimed to explore the adverse influences of mobile phone usage on pilots’ status, so as to improve flight safety.MethodsA questionnaire was designed, and a cluster random sampling method was adopted. Pilots of Shandong Airlines were investigated on the use of mobile phones. The data was analyzed by frequency statistics, linear regression and other statistical methods.ResultsA total of 340 questionnaires were distributed and 317 were returned, 315 of which were valid. The results showed that 239 pilots (75.87%) used mobile phones as the main means of entertainment in their leisure time. There was a significant negative correlation between age of pilots and playing mobile games (p

Related article: Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39.

In this dataset:

We present temporally dynamic population distribution data from the Helsinki Metropolitan Area, Finland, at the level of 250 m by 250 m statistical grid cells. Three hourly population distribution datasets are provided for regular workdays (Mon – Thu), Saturdays and Sundays. The data are based on aggregated mobile phone data collected by the biggest mobile network operator in Finland. Mobile phone data are assigned to statistical grid cells using an advanced dasymetric interpolation method based on ancillary data about land cover, buildings and a time use survey. The data were validated by comparing population register data from Statistics Finland for night-time hours and a daytime workplace registry. The resulting 24-hour population data can be used to reveal the temporal dynamics of the city and examine population variations relevant to for instance spatial accessibility analyses, crisis management and planning.

Please cite this dataset as:

Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39. https://doi.org/10.1038/s41597-021-01113-4

Organization of data

The dataset is packaged into a single Zipfile Helsinki_dynpop_matrix.zip which contains following files:

1. HMA_Dynamic_population_24H_workdays.csv represents the dynamic population for average workday in the study area.
2. HMA_Dynamic_population_24H_sat.csv represents the dynamic population for average saturday in the study area.
3. HMA_Dynamic_population_24H_sun.csv represents the dynamic population for average sunday in the study area.
4. target_zones_grid250m_EPSG3067.geojson represents the statistical grid in ETRS89/ETRS-TM35FIN projection that can be used to visualize the data on a map using e.g. QGIS.

Column names

1. YKR_ID : a unique identifier for each statistical grid cell (n=13,231). The identifier is compatible with the statistical YKR grid cell data by Statistics Finland and Finnish Environment Institute.
2. H0, H1 ... H23 : Each field represents the proportional distribution of the total population in the study area between grid cells during a one-hour period. In total, 24 fields are formatted as “Hx”, where x stands for the hour of the day (values ranging from 0-23). For example, H0 stands for the first hour of the day: 00:00 - 00:59.
   The sum of all cell values for each field equals to 100 (i.e. 100% of total population for each one-hour period)

In order to visualize the data on a map, the result tables can be joined with the target_zones_grid250m_EPSG3067.geojson data. The data can be joined by using the field YKR_ID as a common key between the datasets.

License
Creative Commons Attribution 4.0 International.

Related datasets

• Järv, Olle; Tenkanen, Henrikki & Toivonen, Tuuli. (2017). Multi-temporal function-based dasymetric interpolation tool for mobile phone data. Zenodo. https://doi.org/10.5281/zenodo.252612
• Tenkanen, Henrikki, & Toivonen, Tuuli. (2019). Helsinki Region Travel Time Matrix [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3247564

Chad: Mobile phone subscribers, per 100 people: The latest value from 2023 is 70.19 subscribers per 100 people, an increase from 65.44 subscribers per 100 people in 2022. In comparison, the world average is 120.02 subscribers per 100 people, based on data from 156 countries. Historically, the average for Chad from 1960 to 2023 is 12.59 subscribers per 100 people. The minimum value, 0 subscribers per 100 people, was reached in 1960 while the maximum of 70.19 subscribers per 100 people was recorded in 2023.

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

Lebanon Number Data is a list of phone numbers that you can filter in many ways. You can filter by gender, age, or even relationship status. To contact young people, filter the list to show only numbers from that age group. This helps you connect with the right individual quickly. The list also follows GDPR rules, which means it protects people’s privacy. Furthermore, we regularly update the Lebanon Number Data for clarity. It removes invalid numbers, saving time by avoiding outdated contact details. This feature keeps the list fresh and up-to-date and makes your work more efficient. With Lebanon contact data, you can trust accurate, up-to-date details and filter them to meet your needs. Lebanon phone data is a collection of phone numbers that is 100% correct and valid. The companies that provide this data check every number carefully to make sure it works. So, when you use this cellphone data, you don’t have to worry about the wrong numbers. If, for some reason, a number doesn’t work, you get a replacement guarantee. This means, if a number is invalid, they will give you a new dialing number at no extra cost. Moreover, Lebanon phone data comes with all phone number subscribers’ permission. This means the people who own the numbers have agreed to share their information. It’s very important to have this permission because it keeps you out of legal trouble. Using this database without the customer’s permission can be problematic, but this data is safe and secure. Lebanon phone number list is a collection of phone numbers of people living in Lebanon. This list is very helpful for businesses that need to contact people in Lebanon. The information comes from reliable sources, such as government records, websites, and phone service providers. You can even check the URLs where the data came from. This ensures that the phone numbers are accurate. Also, if you need help, 24/7 support is available. Also, the Lebanon phone number list follows the opt-in rule. Number owners know that others use their info, making it safe to use the data. You won’t face any trouble, and it respects people’s privacy. Using the Lebanon contact number list from our List to Data website, you can confidently connect with the right people.

"Mobile phone usage is a global phenomenon, with billions of people worldwide using smartphones for communication, entertainment, and information. Average daily screen time varies across countries, with some nations spending over 5 hours per day on their devices."

The goal of this study is to measure willingness to participate in passive mobile data collection among German smartphone owners. The data come from a two-wave web survey among German smartphone users 18 years and older who were recruited from a German nonprobability online panel. In December 2016, 2,623 participants completed the Wave 1 questionnaire on smartphone use and skills, privacy and security concerns, and general attitudes towards survey research and research institutions. In January 2017, all respondents from Wave 1 were invited to participate in a second web survey which included vignettes that varied the levels of several dimensions of a hypothetical study using passive mobile data collection, and respondents were asked to rate their willingness to participate in such a study. A total of 1,957 respondents completed the Wave 2 questionnaire.

Wave 1

Topics: Ownership of smartphone, mobile phone, PC, tablet, and/or e-book reader; type of smartphone; frequency of smartphone use; smartphone activities (browsing, e-mails, taking photos, view/ post social media content, shopping, online banking, installing apps, using GPS-enabled apps, connecting via Bluethooth, play games, stream music/ videos); self-assessment of smartphone skills; attitude towards surveys and participaton at research studies (personal interest, waste of time, sales pitch, interesting experience, useful); trust in institutions regarding data privacy (market research companies, university researchers, statistical office, mobile service provider, app companies, credit card companies, online retailer, and social networks); concerns regarding the disclosure of personal data by the aforementioned institutions; general privacy concern; privacy violated by banks/ credit card companies, tax authorities, government agencies, market research companies, social networks, apps, internet browsers); concern regarding data security with smartphone activities for research (online survey, survey apps, research apps, SMS survey, camera, activity data, GPS location, Bluetooth); number of online surveys in which the respondent has participated in the last 30 days; Panel memberships other than that of mingle; previous participation in a study with downloading a research app to the smartphone (passive mobile data collection).

Wave 2

Topics: Willingness to participate in passive mobile data collection (using eight vignettes with different scenarios that varied the levels of several dimensions of a hypothetical study using passive mobile data collection. The research app collects the following data for research purposes: technical characteristics of the smartphone (e.g. phone brand, screen size), the currently used telephone network (e.g. signal strength), the current location (every 5 minutes), which apps are used and which websites are visited, number of incoming and outgoing calls and SMS messages on the smartphone); reason why the respondent wouldn´t (respectively would) participate in the research study used in the first scenario (open answer); recognition of differences between the eight scenarios; kind of recognized difference (open answer); remembered data the research app collects (recall); previous invitation for research app download; research app download.

Demography: sex; age; federal state; highest level of school education; highest level of vocational qualification.

Additionally coded was: running number; respondent ID; duration (response time in seconds); device type used to fill out the questionnaire; vignette text; vignette intro time; vignette time.

In 2022, the average data used per smartphone per month worldwide amounted to ** gigabytes (GB). The source forecasts that this will increase almost four times reaching ** GB per smartphone per month globally in 2028.

Quantitative and qualitative data sets for 24 sites across Ghana, Malawi and South Africa:
a) SPSS dataset on young people’s use of mobile phones in Ghana, Malawi and South Africa.  4626 cases (young people aged 7-25 years): 1568 Ghana; 1544 Malawi; 1514 South Africa.  719 variables (+ 11 ‘navigation facilitators’) b) 1,620 Qualitative transcripts from interviews with people of diverse ages, 8y upwards: individual interviews [using either i.theme checklist or ii call register checklist]; focus group interviews [not all sites]: 50-80 transcripts for most sites.

This research project, which commenced in August 2012, explored how the rapid expansion of mobile phone usage is impacting on young lives in sub-Saharan Africa. It builds directly on our previous research on children’s mobility within which baseline quantitative data and preliminary qualitative information was collected on mobile phone usage (2006-2010) across 24 research sites, as an adjunct to our wider study of children’s physical mobility and access to services.

In this study our focus is specifically on mobile phones and we cover a much wider range of phone-related issues, including changes in gendered and age patterns of phone use over time; phone use in building social networks (for instance to support job search); impacts on education, livelihoods, health status, safety and surveillance, physical mobility and possible connections to migration, youth identity, and questions of exploitation and empowerment associated with mobile phones.

Mixed-method, participatory youth-centred studies have been conducted in the same 24 sites as in our earlier work across Ghana, Malawi and South Africa (urban, peri-urban, rural, remote rural, in two agro-ecological zones per country). We have built on the baseline data for 9-18 year-olds gathered in 2006-2010, through repeat and extended studies, but also included additional studies with 19-25 year-olds (to capture changing usage and its impacts as our initial cohort move into their 20s).

Mobile phone components from Android phones were collected to create this dataset. There are total eight folder (based on mobile phone brand Name) and each of these folder contains 13 sub-folders (based on mobile phone component name). Therefore, a total of 206 images have been collected.

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).

Forecast: Total Mobile Phone Subscriptions in Sweden 2022 - 2026 Discover more data with ReportLinker!

Portugal: Mobile phone subscribers, per 100 people: The latest value from 2023 is 123 subscribers per 100 people, an increase from 122.8 subscribers per 100 people in 2022. In comparison, the world average is 120.02 subscribers per 100 people, based on data from 156 countries. Historically, the average for Portugal from 1960 to 2023 is 52.81 subscribers per 100 people. The minimum value, 0 subscribers per 100 people, was reached in 1960 while the maximum of 132.99 subscribers per 100 people was recorded in 2008.