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Twitterhttps://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
This dataset provides a comprehensive collection of information about all the latest smartphones available in the market as of the current time.
https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13571604%2Fb608498b1cf7f70b9a22952566197db6%2FScreenshot%202023-08-02%20003740.png?generation=1690961033930490&alt=media" alt="">
The dataset was created by web scraping reputable online sources to gather accurate and up-to-date information about various smartphone models, their specifications, features, and pricing.
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TwitterThe 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.
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Twitterhttps://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
"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."
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TwitterIn 2022, smartphone vendors sold around 1.39 billion smartphones were sold worldwide, with this number forecast to drop to 1.34 billion in 2023.
Smartphone penetration rate still on the rise
Less than half of the world’s total population owned a smart device in 2016, but the smartphone penetration rate has continued climbing, reaching 78.05 percent in 2020. By 2025, it is forecast that almost 87 percent of all mobile users in the United States will own a smartphone, an increase from the 27 percent of mobile users in 2010.
Smartphone end user sales
In the United States alone, sales of smartphones were projected to be worth around 73 billion U.S. dollars in 2021, an increase from 18 billion dollars in 2010. Global sales of smartphones are expected to increase from 2020 to 2021 in every major region, as the market starts to recover from the initial impact of the coronavirus (COVID-19) pandemic.
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TwitterThe 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).
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TwitterApache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
License information was derived automatically
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.
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TwitterThe number of smartphone users in France was forecast to continuously increase between 2024 and 2029 by in total 3.2 million users (+5.96 percent). After the fifteenth consecutive increasing year, the smartphone user base is estimated to reach 56.89 million users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smartphone users in countries like Belgium and Luxembourg.
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TwitterMIT Licensehttps://opensource.org/licenses/MIT
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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.
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.
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.).
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.
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Twitterlicense: apache-2.0 tags: - africa - sustainable-development-goals - world-health-organization - development
Individuals who own a mobile telephone (%)
Dataset Description
This dataset provides country-level data for the indicator "5.b.1 Individuals who own a mobile telephone (%)" across African nations, sourced from the World Health Organization's (WHO) data portal on Sustainable Development Goals (SDGs). The data is presented in a wide format, where each row… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/mobile-phone-ownership-for-african-countries.
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Description: Welcome to this comprehensive dataset exploring the detrimental effects of defective products on both product revenue and global brand image. This dataset presents an in-depth examination of the consequences of product defects on businesses and their market reputation, providing valuable insights for researchers, analysts, and stakeholders.
Key Features:
The dataset contains detailed records of various products that were found to be defective, along with information on their respective industries and categories.
Gain a deeper understanding of customer sentiments and feedback related to defective products. Analyze customer reviews, complaints, and social media reactions to gauge the extent of damage to brand perception.
The dataset covers a significant time period, allowing for the analysis of trends and long-term consequences of product defects on financial performance and brand reputation.
The dataset includes information on how users responded to defective product incidents. Analyze the effectiveness of recall campaigns, corrective actions, and communication strategies.
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TwitterMIT Licensehttps://opensource.org/licenses/MIT
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This dataset provides detailed insights into mobile phone operating systems, covering their versions, release years, market shares, and technical capabilities. It includes 15,000 rows of data across 15 attributes, making it valuable for data analysis, machine learning, and trend forecasting. Researchers and developers can use this dataset to analyze OS adoption rates, security improvements, and technological advancements over time.
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TwitterGlobal B2B Mobile Phone Number Database | 100M+ Verified Contacts | 95% Accuracy Forager.ai provides the world’s most reliable mobile phone number data for businesses that refuse to compromise on quality. With 100 million+ professionally verified mobile numbers refreshed every 3 weeks, our database ensures 95% accuracy – so your teams never waste time on dead-end leads.
Why Our Data Wins ✅ Accuracy You Can Trust 95% of mobile numbers are verified against live carrier records and tied to current job roles. Say goodbye to “disconnected number” voicemails.
✅ Depth Beyond Digits Each contact includes 150+ data points:
Direct mobile numbers
Current job title, company, and department
Full career history + education background
Location data + LinkedIn profiles
Company size, industry, and revenue
✅ Freshness Guaranteed Bi-weekly updates combat job-hopping and role changes – critical for sales teams targeting decision-makers.
✅ Ethically Sourced & Compliant First-party collected data with full GDPR/CCPA compliance.
Who Uses This Data?
Sales Teams: Cold-call C-suite prospects with verified mobile numbers.
Marketers: Run hyper-personalized SMS/WhatsApp campaigns.
Recruiters: Source passive candidates with up-to-date contact intel.
Data Vendors: License premium datasets to enhance your product.
Tech Platforms: Power your SaaS tools via API with enterprise-grade B2B data.
Flexible Delivery, Instant Results
API (REST): Real-time integration for CRMs, dialers, or marketing stacks
CSV/JSON: Campaign-ready files.
PostgreSQL: Custom databases for large-scale enrichment
Compliance: Full audit trails + opt-out management
Why Forager.ai? → Proven ROI: Clients see 62% higher connect rates vs. industry averages (request case studies). → No Guesswork: Test-drive free samples before committing. → Scalable Pricing: Pay per record, license datasets, or get unlimited API access.
B2B Mobile Phone Data | Verified Contact Database | Sales Prospecting Lists | CRM Enrichment | Recruitment Phone Numbers | Marketing Automation | Phone Number Datasets | GDPR-Compliant Leads | Direct Dial Contacts | Decision-Maker Data
Need Proof? Contact us to see why Fortune 500 companies and startups alike trust Forager.ai for mission-critical outreach.
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Twitterhttp://opendatacommons.org/licenses/dbcl/1.0/http://opendatacommons.org/licenses/dbcl/1.0/
Internet users (per 100 people) Internet users are individuals who have used the Internet (from any location) in the last 12 months. Internet can be used via a computer, mobile phone, personal digital assistant, games machine, digital TV etc.
SourceInternational Telecommunication Union, World Telecommunication/ICT Development Report and database, and World Bank estimates. License Open Aggregation Method: Weighted average Development Relevance: The digital and information revolution has changed the way the world learns, communicates, does business, and treats illnesses. New information and communications technologies (ICT) offer vast opportunities for progress in all walks of life in all countries - opportunities for economic growth, improved health, better service delivery, learning through distance education, and social and cultural advances. Today's smartphones and tablets have computer power equivalent to that of yesterday's computers and provide a similar range of functions. Device convergence is thus rendering the conventional definition obsolete. Comparable statistics on access, use, quality, and affordability of ICT are needed to formulate growth-enabling policies for the sector and to monitor and evaluate the sector's impact on development. Although basic access data are available for many countries, in most developing countries little is known about who uses ICT; what they are used for (school, work, business, research, government); and how they affect people and businesses. The global Partnership on Measuring ICT for Development is helping to set standards, harmonize information and communications technology statistics, and build statistical capacity in developing countries. However, despite significant improvements in the developing world, the gap between the ICT haves and have-nots remains. General Comments: Please cite the International Telecommunication Union for third-party use of these data. Limitations and Exceptions: Operators have traditionally been the main source of telecommunications data, so information on subscriptions has been widely available for most countries. This gives a general idea of access, but a more precise measure is the penetration rate - the share of households with access to telecommunications. During the past few years more information on information and communication technology use has become available from household and business surveys. Also important are data on actual use of telecommunications services. Ideally, statistics on telecommunications (and other information and communications technologies) should be compiled for all three measures: subscriptions, access, and use. The quality of data varies among reporting countries as a result of differences in regulations covering data provision and availability. Discrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is the end of December of every year. A number of countries have fiscal years that end in March or June of every year. Periodicity: Annual Statistical Concept and Methodology: The Internet is a world-wide public computer network. It provides access to a number of communication services including the World Wide Web and carries email, news, entertainment and data files, irrespective of the device used (not assumed to be only via a computer - it may also be by mobile phone, PDA, games machine, digital TV etc.). Access can be via a fixed or mobile network.
Your data will be in front of the world's largest data science community. What questions do you want to see answered?
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TwitterChina is leading the ranking by number of smartphone users, recording ****** million users. Following closely behind is India with ****** million users, while Seychelles is trailing the ranking with **** million users, resulting in a difference of ****** million users to the ranking leader, China. 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).
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TwitterThe number of smartphone users in the United Kingdom was forecast to continuously increase between 2024 and 2029 by in total *** million users (+**** percent). After the ninth consecutive increasing year, the smartphone user base is estimated to reach ***** 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 *** 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 information concerning Denmark and Latvia.
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TwitterHow many people use social media?
Social media usage is one of the most popular online activities. In 2024, over five billion people were using social media worldwide, a number projected to increase to over six billion in 2028.
Who uses social media?
Social networking is one of the most popular digital activities worldwide and it is no surprise that social networking penetration across all regions is constantly increasing. As of January 2023, the global social media usage rate stood at 59 percent. This figure is anticipated to grow as lesser developed digital markets catch up with other regions
when it comes to infrastructure development and the availability of cheap mobile devices. In fact, most of social media’s global growth is driven by the increasing usage of mobile devices. Mobile-first market Eastern Asia topped the global ranking of mobile social networking penetration, followed by established digital powerhouses such as the Americas and Northern Europe.
How much time do people spend on social media?
Social media is an integral part of daily internet usage. On average, internet users spend 151 minutes per day on social media and messaging apps, an increase of 40 minutes since 2015. On average, internet users in Latin America had the highest average time spent per day on social media.
What are the most popular social media platforms?
Market leader Facebook was the first social network to surpass one billion registered accounts and currently boasts approximately 2.9 billion monthly active users, making it the most popular social network worldwide. In June 2023, the top social media apps in the Apple App Store included mobile messaging apps WhatsApp and Telegram Messenger, as well as the ever-popular app version of Facebook.
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Twitterhttp://opendatacommons.org/licenses/dbcl/1.0/http://opendatacommons.org/licenses/dbcl/1.0/
This dataset provides detailed information about various smartphones available on the e-commerce platform Flipkart as of July 2023. The dataset is in CSV format and contains the following columns:
img_link: This column contains the URL link to the image of each smartphone. It can be used to retrieve and display the corresponding image for each smartphone.
phone_name: This column contains the name or model of each smartphone. It provides a unique identifier for each device in the dataset.
avg_rating: This column represents the average rating of each smartphone on Flipkart. It indicates the overall customer satisfaction level based on user ratings. The rating scale typically ranges from 1 to 5 stars, with 5 being the highest rating.
total_rating: This column indicates the total number of people who have rated each smartphone on Flipkart. It provides an understanding of the popularity and feedback from customers who have shared their ratings.
total_reviews: This column represents the total number of reviews available for each smartphone on Flipkart. It provides insights into the level of engagement and the amount of user-generated content related to each device.
discounted_price: This column contains the discounted price of each smartphone in Indian Rupees (INR). It represents the current selling price of the device after applying any applicable discounts or promotional offers.
actual_price: This column displays the actual or original price of each smartphone in Indian Rupees (INR) before any discounts. It provides a reference point for the discounted price and helps users understand the amount of savings or price reduction available.
The dataset is valuable for conducting various analyses related to smartphones available on Flipkart. Researchers, data scientists, and analysts can use this dataset to explore trends in customer ratings, reviews, pricing, and discounts. They can also perform market research, brand comparisons, sentiment analysis, and other studies related to the smartphone industry.
Please note that this dataset is specific to Flipkart and represents the smartphone market as of July 2023. It can be used to gain insights into customer preferences, pricing strategies, and overall market dynamics in the context of Flipkart's smartphone offerings.
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TwitterThe World Bank in collaboration with the Kenya National Bureau of Statistics and the University of California, Berkeley are conducting the Kenya COVID-19 Rapid Response Phone Survey to track the socioeconomic impacts of the COVID-19 pandemic, the recovery from it as well as other shocks to provide timely data to inform policy. This dataset contains information from eight waves of the COVID-19 RRPS, which is part of a panel survey that targets Kenyan nationals and started in May 2020. The same households were interviewed every two months for five survey rounds, in the first year of data collection and every four months thereafter, with interviews conducted using Computer Assisted Telephone Interviewing (CATI) techniques.
The data set contains information from two samples of Kenyan households. The first sample is a randomly drawn subset of all households that were part of the 2015/16 Kenya Integrated Household Budget Survey (KIHBS) Computer-Assisted Personal Interviewing (CAPI) pilot and provided a phone number. The second was obtained through the Random Digit Dialing method, by which active phone numbers created from the 2020 Numbering Frame produced by the Kenya Communications Authority are randomly selected. The samples cover urban and rural areas and are designed to be representative of the population of Kenya using cell phones. Waves 1-7 of this survey include information on household background, service access, employment, food security, income loss, transfers, health, and COVID-19 knowledge and vaccinations. Wave 8 focused on how households were exposed to shocks, in particular adverse weather shocks and the increase in the price of food and fuel, but also included parts of the previous modules on household background, service access, employment, food security, income loss, and subjective wellbeing.
The data is uploaded in three files. The first is the hh file, which contains household level information. The ‘hhid’, uniquely identifies all household. The second is the adult level file, which contains data at the level of adult household members. Each adult in a household is uniquely identified by the ‘adult_id’. The third file is the child level file, available only for waves 3-7, which contains information for every child in the household. Each child in a household is uniquely identified by the ‘child_id’.
The duration of data collection and sample size for each completed wave was: Wave 1: May 14 to July 7, 2020; 4,061 Kenyan households Wave 2: July 16 to September 18, 2020; 4,492 Kenyan households Wave 3: September 28 to December 2, 2020; 4,979 Kenyan households Wave 4: January 15 to March 25, 2021; 4,892 Kenyan households Wave 5: March 29 to June 13, 2021; 5,854 Kenyan households Wave 6: July 14 to November 3, 2021; 5,765 Kenyan households Wave 7: November 15, 2021, to March 31, 2022; 5,633 Kenyan households Wave 8: May 31 to July 8, 2022: 4,550 Kenyan households
The same questionnaire is also administered to refugees in Kenya, with the data available in the UNHCR microdata library: https://microdata.unhcr.org/index.php/catalog/296/
National coverage covering rural and urban areas
Household, Individual
The COVID-19 RRPS with Kenyan households has two samples. The first sample consists of households that were part of the 2015/16 KIHBS CAPI pilot and provided a phone number. The 2015/16 KIHBS CAPI pilot is representative at the national level stratified by county and place of residence (urban and rural areas). At least one valid phone number was obtained for 9,007 households and all of them were included in the COVID-19 RRPS sample. The target respondent was the primary male or female household member from the 2015/16 KIHBS CAPI pilot. The second sample consists of households selected using the Random Digit Dialing method. A list of random mobile phone numbers was created using a random number generator from the 2020 Numbering Frame produced by the Kenya Communications Authority. The initial sampling frame therefore consisted of 92,999,970 randomly ordered phone numbers assigned to three networks: Safaricom, Airtel and Telkom. An introductory text message was sent to 5,000 randomly selected numbers to determine if numbers were in operation. Out of these, 4,075 were found to be active and formed the final sampling frame. There was no stratification and individuals that were called were asked about the households they live in. Until wave 7 sampled households that were not reached in earlier waves were also contacted along with households that were interviewed before. In wave 8 only households that had previously participated in the survey were contacted for interview. The “wave” variable represents in which wave the households were interviewed in.
Computer Assisted Personal Interview [capi]
The questionnaire was administered in English and is provided as a resource in pdf format. Additionally, questionnaires for each wave are also provided in Excel format coded for SCTO. The same questionnaire is also administered to refugees in Kenya, with the data available in the UNHCR microdata library: https://microdata.unhcr.org/index.php/catalog/296/
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Pakistan PK: Internet Users: Individuals: % of Population data was reported at 15.515 % in 2016. This records an increase from the previous number of 14.000 % for 2015. Pakistan PK: Internet Users: Individuals: % of Population data is updated yearly, averaging 6.416 % from Dec 1990 (Median) to 2016, with 22 observations. The data reached an all-time high of 15.515 % in 2016 and a record low of 0.000 % in 1990. Pakistan PK: Internet Users: Individuals: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Pakistan – Table PK.World Bank: Telecommunication. Internet users are individuals who have used the Internet (from any location) in the last 3 months. The Internet can be used via a computer, mobile phone, personal digital assistant, games machine, digital TV etc.; ; International Telecommunication Union, World Telecommunication/ICT Development Report and database.; Weighted average; Please cite the International Telecommunication Union for third-party use of these data.
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TwitterCC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
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India phone surveyThere are a total of 19 questions in the survey, addressing the following categories:Internet useMobile phone use (smartphones & basic voice/SMS phones)Awareness and use of WikipediaThe 2016 Indian phone survey is a composite of 7 individual regional surveys. The survey covered over 90% of India's geography, gathering over 9000 full responses from a set of 12 languages presented. Here are the main questions this survey was designed to answer. However, analyzing the full data set allows you to conduct more in-depth data explorations and gain meaningful insights beyond the points presented here:What is the actual number of people who use the internet?(Real-world behavior makes this difficult to measure from industry reports, since people might have access to the internet through school, friends, internet cafés, public Wifi, etc.)For internet users: What do people mostly use the internet for?For non-internet users: Why not use the internet?How many people use smartphones?Do people with smartphones use the internet from just Wifi? Or just cellular service?How many people think that they don’t use the internet, but still use Facebook or WhatsApp?How many people have heard of Wikipedia? What do they use it for? How often?If they have heard of Wikipedia, but aren’t using it, why not?
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Twitterhttps://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
This dataset provides a comprehensive collection of information about all the latest smartphones available in the market as of the current time.
https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13571604%2Fb608498b1cf7f70b9a22952566197db6%2FScreenshot%202023-08-02%20003740.png?generation=1690961033930490&alt=media" alt="">
The dataset was created by web scraping reputable online sources to gather accurate and up-to-date information about various smartphone models, their specifications, features, and pricing.