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.

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.

Looking to gain insights into the world of mobile phones? Look no further than our comprehensive dataset, which provides detailed specifications and prices for a wide range of smartphones. With data on everything from screen size and camera quality to battery life and processing power, this dataset is a must-have for anyone interested in the mobile phone market. Whether you're a researcher, a tech enthusiast, or just looking to make an informed purchase, our data will give you the information you need to make smart decisions. So why wait? Download our dataset today and start exploring the world of mobile phones like never before! The prices are in PKR. as the dataset is extracted from Pakistan Mobile market website

In 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.

Context

Android is the most used operating systems in the mobile phones field, it would be interesting to explore the different manufacturers and devices that uses it and which versions of Android operating system are widely used

Content

The data has about 1300 rows including 4 attributes described as following:

Name: Mobile phone name Brand: Manufacturer brand name Release: Release date of the mobile Version: Android version of the mobile

Questions to be answered

How many phones use Android 11 ? Which phones were released the latest ? Which brand has the most phones released ? How many brands are there

Series Name: Proportion of individuals who own a mobile telephone by sex (percent)Series Code: IT_MOB_OWNRelease Version: 2020.Q2.G.03This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 5.b.1: Proportion of individuals who own a mobile telephone, by sexTarget 5.b: Enhance the use of enabling technology, in particular information and communications technology, to promote the empowerment of womenGoal 5: Achieve gender equality and empower all women and girlsFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

China 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).

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).

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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.

Abstract

The Global Findex 2025 reveals how mobile technology is equipping more adults around the world to own and use financial accounts to save formally, access credit, make and receive digital payments, and pursue opportunities. Including the inaugural Global Findex Digital Connectivity Tracker, this fifth edition of Global Findex presents new insights on the interactions among mobile phone ownership, internet use, and financial inclusion.

The Global Findex is the world’s most comprehensive database on digital and financial inclusion. It is also the only global source of comparable demand-side data, allowing cross-country analysis of how adults access and use mobile phones, the internet, and financial accounts to reach digital information and resources, save, borrow, make payments, and manage their financial health. Data for the Global Findex 2025 were collected from nationally representative surveys of about 145,000 adults in 141 economies. The latest edition follows the 2011, 2014, 2017, and 2021 editions and includes new series measuring mobile phone ownership and internet use, digital safety, and frequency of transactions using financial services.

The Global Findex 2025 is an indispensable resource for policy makers in the fields of digital connectivity and financial inclusion, as well as for practitioners, researchers, and development professionals.

Geographic coverage

National Coverage

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most low- and middle-income economies, Global Findex data were collected through face-to-face interviews. In these economies, an area frame design was used for interviewing. In most high-income economies, telephone surveys were used. In 2024, face-to-face interviews were again conducted in 22 economies after phone-based surveys had been employed in 2021 as a result of mobility restrictions related to COVID-19. In addition, an abridged form of the questionnaire was administered by phone to survey participants in Algeria, China, the Islamic Republic of Iran, Libya, Mauritius, and Ukraine because of economy-specific restrictions. In just one economy, Singapore, did the interviewing mode change from face to face in 2021 to phone based in 2024.

In economies in which face-to-face surveys were conducted, the first stage of sampling was the identification of primary sampling units. These units were then stratified by population size, geography, or both and clustered through one or more stages of sampling. Where population information was available, sample selection was based on probabilities proportional to population size; otherwise, simple random sampling was used. Random route procedures were used to select sampled households. Unless an outright refusal occurred, interviewers made up to three attempts to survey each sampled household. To increase the probability of contact and completion, attempts were made at different times of the day and, where possible, on different days. If an interview could not be completed at a household that was initially part of the sample, a simple substitution method was used to select a replacement household for inclusion.

Respondents were randomly selected within sampled households. Each eligible household member (that is, all those ages 15 or older) was listed, and a handheld survey device randomly selected the household member to be interviewed. For paper surveys, the Kish grid method was used to select the respondent. In economies in which cultural restrictions dictated gender matching, respondents were randomly selected from among all eligible adults of the interviewer’s gender.

In economies in which Global Findex surveys have traditionally been phone based, respondent selection followed the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies in which mobile phone and landline penetration is high, a dual sampling frame was used.

The same procedure for respondent selection was applied to economies in which phone-based interviews were being conducted for the first time. Dual-frame (landline and mobile phone) random digit dialing was used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digit dialing was used in economies with limited or no landline presence (less than 20 percent). For landline respondents in economies in which mobile phone or landline penetration is 80 percent or higher, respondents were selected randomly by using either the next-birthday method or the household enumeration method, which involves listing all eligible household members and randomly selecting one to participate. For mobile phone respondents in these economies or in economies in which mobile phone or landline penetration is less than 80 percent, no further selection was performed. At least three attempts were made to reach the randomly selected person in each household, spread over different days and times of day.

Research instrument

The English version of the questionnaire is provided for download.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in: Klapper, Leora, Dorothe Singer, Laura Starita, and Alexandra Norris. 2025. The Global Findex Database 2025: Connectivity and Financial Inclusion in the Digital Economy. Washington, DC: World Bank. https://doi.org/10.1596/978-1-4648-2204-9.

In the third quarter of 2025, Samsung shipped approximately **** million smartphones, marking an increase compared to both the previous quarter and the same period in the prior year. The company’s strong sales performance consistently positions Samsung as the world’s leading smartphone vendor, ahead of Apple. Samsung smartphone sales – how many phones does Samsung sell? Global smartphone sales reached over *** billion units during 2024. While the global smartphone market is led by Samsung and Apple, Xiaomi has gained ground following the decline of Huawei. Together, these three companies hold more than ** percent of the global smartphone market share.

Abstract

Well-functioning financial systems serve a vital purpose, offering savings, credit, payment, and risk management products to people with a wide range of needs. Yet until now little had been known about the global reach of the financial sector - the extent of financial inclusion and the degree to which such groups as the poor, women, and youth are excluded from formal financial systems. Systematic indicators of the use of different financial services had been lacking for most economies.

The Global Financial Inclusion (Global Findex) database provides such indicators. This database contains the first round of Global Findex indicators, measuring how adults in more than 140 economies save, borrow, make payments, and manage risk. The data set can be used to track the effects of financial inclusion policies globally and develop a deeper and more nuanced understanding of how people around the world manage their day-to-day finances. By making it possible to identify segments of the population excluded from the formal financial sector, the data can help policy makers prioritize reforms and design new policies.

Geographic coverage

National Coverage.

Analysis unit

Individual

Universe

The target population is the civilian, non-institutionalized population 15 years and above. The sample is nationally representative.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Global Findex indicators are drawn from survey data collected by Gallup, Inc. over the 2011 calendar year, covering more than 150,000 adults in 148 economies and representing about 97 percent of the world's population. Since 2005, Gallup has surveyed adults annually around the world, using a uniform methodology and randomly selected, nationally representative samples. The second round of Global Findex indicators was collected in 2014 and is forthcoming in 2015. The set of indicators will be collected again in 2017.

Surveys were conducted face-to-face in economies where landline telephone penetration is less than 80 percent, or where face-to-face interviewing is customary. The first stage of sampling is the identification of primary sampling units, consisting of clusters of households. The primary sampling units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households by means of the Kish grid.

Surveys were conducted by telephone in economies where landline telephone penetration is over 80 percent. The telephone surveys were conducted using random digit dialing or a nationally representative list of phone numbers. In selected countries where cell phone penetration is high, a dual sampling frame is used. Random respondent selection is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to teach a person in each household, spread over different days and times of year.

The sample size in Japan was 1,000 individuals.

Mode of data collection

Landline telephone

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup, Inc. also provided valuable input. The questionnaire was piloted in over 20 countries using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on insurance, mobile payments, and loan purposes were asked only in developing economies. The indicators on awareness and use of microfinance insitutions (MFIs) are not included in the public dataset. However, adults who report saving at an MFI are considered to have an account; this is reflected in the composite account indicator.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country- and indicator-specific standard errors, refer to the Annex and Country Table in Demirguc-Kunt, Asli and L. Klapper. 2012. "Measuring Financial Inclusion: The Global Findex." Policy Research Working Paper 6025, World Bank, Washington, D.C.

Abstract

The Global Findex 2025 reveals how mobile technology is equipping more adults around the world to own and use financial accounts to save formally, access credit, make and receive digital payments, and pursue opportunities. Including the inaugural Global Findex Digital Connectivity Tracker, this fifth edition of Global Findex presents new insights on the interactions among mobile phone ownership, internet use, and financial inclusion.

The Global Findex is the world’s most comprehensive database on digital and financial inclusion. It is also the only global source of comparable demand-side data, allowing cross-country analysis of how adults access and use mobile phones, the internet, and financial accounts to reach digital information and resources, save, borrow, make payments, and manage their financial health. Data for the Global Findex 2025 were collected from nationally representative surveys of about 145,000 adults in 141 economies. The latest edition follows the 2011, 2014, 2017, and 2021 editions and includes new series measuring mobile phone ownership and internet use, digital safety, and frequency of transactions using financial services.

The Global Findex 2025 is an indispensable resource for policy makers in the fields of digital connectivity and financial inclusion, as well as for practitioners, researchers, and development professionals.

Geographic coverage

National Coverage

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most low- and middle-income economies, Global Findex data were collected through face-to-face interviews. In these economies, an area frame design was used for interviewing. In most high-income economies, telephone surveys were used. In 2024, face-to-face interviews were again conducted in 22 economies after phone-based surveys had been employed in 2021 as a result of mobility restrictions related to COVID-19. In addition, an abridged form of the questionnaire was administered by phone to survey participants in Algeria, China, the Islamic Republic of Iran, Libya, Mauritius, and Ukraine because of economy-specific restrictions. In just one economy, Singapore, did the interviewing mode change from face to face in 2021 to phone based in 2024.

In economies in which face-to-face surveys were conducted, the first stage of sampling was the identification of primary sampling units. These units were then stratified by population size, geography, or both and clustered through one or more stages of sampling. Where population information was available, sample selection was based on probabilities proportional to population size; otherwise, simple random sampling was used. Random route procedures were used to select sampled households. Unless an outright refusal occurred, interviewers made up to three attempts to survey each sampled household. To increase the probability of contact and completion, attempts were made at different times of the day and, where possible, on different days. If an interview could not be completed at a household that was initially part of the sample, a simple substitution method was used to select a replacement household for inclusion.

Respondents were randomly selected within sampled households. Each eligible household member (that is, all those ages 15 or older) was listed, and a handheld survey device randomly selected the household member to be interviewed. For paper surveys, the Kish grid method was used to select the respondent. In economies in which cultural restrictions dictated gender matching, respondents were randomly selected from among all eligible adults of the interviewer’s gender.

In economies in which Global Findex surveys have traditionally been phone based, respondent selection followed the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies in which mobile phone and landline penetration is high, a dual sampling frame was used.

The same procedure for respondent selection was applied to economies in which phone-based interviews were being conducted for the first time. Dual-frame (landline and mobile phone) random digit dialing was used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digit dialing was used in economies with limited or no landline presence (less than 20 percent). For landline respondents in economies in which mobile phone or landline penetration is 80 percent or higher, respondents were selected randomly by using either the next-birthday method or the household enumeration method, which involves listing all eligible household members and randomly selecting one to participate. For mobile phone respondents in these economies or in economies in which mobile phone or landline penetration is less than 80 percent, no further selection was performed. At least three attempts were made to reach the randomly selected person in each household, spread over different days and times of day.

Research instrument

The English version of the questionnaire is provided for download.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in: Klapper, Leora, Dorothe Singer, Laura Starita, and Alexandra Norris. 2025. The Global Findex Database 2025: Connectivity and Financial Inclusion in the Digital Economy. Washington, DC: World Bank. https://doi.org/10.1596/978-1-4648-2204-9.

This competition involves advertisement data provided by BuzzCity Pte. Ltd. BuzzCity is a global mobile advertising network that has millions of consumers around the world on mobile phones and devices. In Q1 2012, over 45 billion ad banners were delivered across the BuzzCity network consisting of more than 10,000 publisher sites which reach an average of over 300 million unique users per month. The number of smartphones active on the network has also grown significantly. Smartphones now account for more than 32% phones that are served advertisements across the BuzzCity network. The "raw" data used in this competition has two types: publisher database and click database, both provided in CSV format. The publisher database records the publisher's (aka partner's) profile and comprises several fields:

publisherid - Unique identifier of a publisher. Bankaccount - Bank account associated with a publisher (may be empty) address - Mailing address of a publisher (obfuscated; may be empty) status - Label of a publisher, which can be the following: "OK" - Publishers whom BuzzCity deems as having healthy traffic (or those who slipped their detection mechanisms) "Observation" - Publishers who may have just started their traffic or their traffic statistics deviates from system wide average. BuzzCity does not have any conclusive stand with these publishers yet "Fraud" - Publishers who are deemed as fraudulent with clear proof. Buzzcity suspends their accounts and their earnings will not be paid

On the other hand, the click database records the click traffics and has several fields:

id - Unique identifier of a particular click numericip - Public IP address of a clicker/visitor deviceua - Phone model used by a clicker/visitor publisherid - Unique identifier of a publisher adscampaignid - Unique identifier of a given advertisement campaign usercountry - Country from which the surfer is clicktime - Timestamp of a given click (in YYYY-MM-DD format) publisherchannel - Publisher's channel type, which can be the following: ad - Adult sites co - Community es - Entertainment and lifestyle gd - Glamour and dating in - Information mc - Mobile content pp - Premium portal se - Search, portal, services referredurl - URL where the ad banners were clicked (obfuscated; may be empty). More details about the HTTP Referer protocol can be found in this article. Related Publication: R. J. Oentaryo, E.-P. Lim, M. Finegold, D. Lo, F.-D. Zhu, C. Phua, E.-Y. Cheu, G.-E. Yap, K. Sim, M. N. Nguyen, K. Perera, B. Neupane, M. Faisal, Z.-Y. Aung, W. L. Woon, W. Chen, D. Patel, and D. Berrar. (2014). Detecting click fraud in online advertising: A data mining approach, Journal of Machine Learning Research, 15, 99-140.

Abstract

The Global Findex 2025 reveals how mobile technology is equipping more adults around the world to own and use financial accounts to save formally, access credit, make and receive digital payments, and pursue opportunities. Including the inaugural Global Findex Digital Connectivity Tracker, this fifth edition of Global Findex presents new insights on the interactions among mobile phone ownership, internet use, and financial inclusion.

The Global Findex is the world’s most comprehensive database on digital and financial inclusion. It is also the only global source of comparable demand-side data, allowing cross-country analysis of how adults access and use mobile phones, the internet, and financial accounts to reach digital information and resources, save, borrow, make payments, and manage their financial health. Data for the Global Findex 2025 were collected from nationally representative surveys of about 145,000 adults in 141 economies. The latest edition follows the 2011, 2014, 2017, and 2021 editions and includes new series measuring mobile phone ownership and internet use, digital safety, and frequency of transactions using financial services.

The Global Findex 2025 is an indispensable resource for policy makers in the fields of digital connectivity and financial inclusion, as well as for practitioners, researchers, and development professionals.

Geographic coverage

National Coverage

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most low- and middle-income economies, Global Findex data were collected through face-to-face interviews. In these economies, an area frame design was used for interviewing. In most high-income economies, telephone surveys were used. In 2024, face-to-face interviews were again conducted in 22 economies after phone-based surveys had been employed in 2021 as a result of mobility restrictions related to COVID-19. In addition, an abridged form of the questionnaire was administered by phone to survey participants in Algeria, China, the Islamic Republic of Iran, Libya, Mauritius, and Ukraine because of economy-specific restrictions. In just one economy, Singapore, did the interviewing mode change from face to face in 2021 to phone based in 2024.

In economies in which face-to-face surveys were conducted, the first stage of sampling was the identification of primary sampling units. These units were then stratified by population size, geography, or both and clustered through one or more stages of sampling. Where population information was available, sample selection was based on probabilities proportional to population size; otherwise, simple random sampling was used. Random route procedures were used to select sampled households. Unless an outright refusal occurred, interviewers made up to three attempts to survey each sampled household. To increase the probability of contact and completion, attempts were made at different times of the day and, where possible, on different days. If an interview could not be completed at a household that was initially part of the sample, a simple substitution method was used to select a replacement household for inclusion.

Respondents were randomly selected within sampled households. Each eligible household member (that is, all those ages 15 or older) was listed, and a handheld survey device randomly selected the household member to be interviewed. For paper surveys, the Kish grid method was used to select the respondent. In economies in which cultural restrictions dictated gender matching, respondents were randomly selected from among all eligible adults of the interviewer’s gender.

In economies in which Global Findex surveys have traditionally been phone based, respondent selection followed the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies in which mobile phone and landline penetration is high, a dual sampling frame was used.

The same procedure for respondent selection was applied to economies in which phone-based interviews were being conducted for the first time. Dual-frame (landline and mobile phone) random digit dialing was used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digit dialing was used in economies with limited or no landline presence (less than 20 percent). For landline respondents in economies in which mobile phone or landline penetration is 80 percent or higher, respondents were selected randomly by using either the next-birthday method or the household enumeration method, which involves listing all eligible household members and randomly selecting one to participate. For mobile phone respondents in these economies or in economies in which mobile phone or landline penetration is less than 80 percent, no further selection was performed. At least three attempts were made to reach the randomly selected person in each household, spread over different days and times of day.

Research instrument

The English version of the questionnaire is provided for download.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in: Klapper, Leora, Dorothe Singer, Laura Starita, and Alexandra Norris. 2025. The Global Findex Database 2025: Connectivity and Financial Inclusion in the Digital Economy. Washington, DC: World Bank. https://doi.org/10.1596/978-1-4648-2204-9.

Dataset Overview This dataset provides a curated, example-based snapshot of selected Samsung smartphones released (or expected to be released) between 2020 and 2024. It includes various technical specifications such as camera details, processor type, RAM, internal storage, display size, GPU, battery capacity, operating system, and pricing. Note that these values are illustrative and may not reflect actual market data.

What’s Inside?

Phone Name & Release Year: Quickly reference the time frame and model. Camera Specs: Understand the rear camera configurations (e.g., “108+10+10+12 MP”) and compare imaging capabilities across models. Processor & GPU: Gain insights into the performance capabilities by checking the processor and graphics chip. Memory & Storage: Review RAM and internal storage options (e.g., “8 GB RAM” and “128 GB Internal Storage”). Display & Battery: Compare screen sizes (from 6.1 to over 7 inches) and battery capacities (e.g., 5000 mAh) to gauge device longevity and usability. Operating System: Note the Android version at release. Price (USD): Examine relative pricing trends over the years. How to Use This Dataset

Exploratory Data Analysis (EDA): Use Python libraries like Pandas and Matplotlib to explore pricing trends over time, changes in camera configurations, or the evolution of battery capacities.

Example: df.groupby('Release Year')['Price (USD)'].mean().plot(kind='bar') can show how average prices have fluctuated year to year. Feature Comparison & Filtering: Easily filter models based on specs. For instance, query phones with at least 8 GB RAM and a 5000 mAh battery to identify devices suitable for power users.

Example: df[(df['RAM (GB)'] >= 8) & (df['Battery Capacity (mAh)'] >= 5000)] Machine Learning & Predictive Analysis: Although this dataset is example-based and not suitable for precise forecasting, you could still practice predictive modeling. For example, create a simple regression model to predict price based on features like RAM and display size.

Example: Train a regression model (e.g., LinearRegression in scikit-learn) to see if increasing RAM or battery capacity correlates with higher prices. Comparing Release Trends: Investigate how flagship and mid-range specifications have evolved. See if there’s a noticeable shift towards larger displays, bigger batteries, or higher camera megapixels over the years.

Recommended Tools & Libraries

Python & Pandas: For data cleaning, manipulation, and initial analysis. Matplotlib & Seaborn: For creating visualizations to understand trends and distributions. scikit-learn: For modeling and basic predictive tasks, if you choose to use these example values as a training ground. Jupyter Notebooks or Kaggle Kernels: For interactive analysis and iterative exploration. Disclaimer This dataset is a synthetic, illustrative example and may not match real-world specifications, prices, or release timelines. It’s intended for learning, experimentation, and demonstration of various data analysis and machine learning techniques rather than as a factual source.

Abstract

The Global Findex 2025 reveals how mobile technology is equipping more adults around the world to own and use financial accounts to save formally, access credit, make and receive digital payments, and pursue opportunities. Including the inaugural Global Findex Digital Connectivity Tracker, this fifth edition of Global Findex presents new insights on the interactions among mobile phone ownership, internet use, and financial inclusion.

The Global Findex is the world’s most comprehensive database on digital and financial inclusion. It is also the only global source of comparable demand-side data, allowing cross-country analysis of how adults access and use mobile phones, the internet, and financial accounts to reach digital information and resources, save, borrow, make payments, and manage their financial health. Data for the Global Findex 2025 were collected from nationally representative surveys of about 145,000 adults in 141 economies. The latest edition follows the 2011, 2014, 2017, and 2021 editions and includes new series measuring mobile phone ownership and internet use, digital safety, and frequency of transactions using financial services.

The Global Findex 2025 is an indispensable resource for policy makers in the fields of digital connectivity and financial inclusion, as well as for practitioners, researchers, and development professionals.

Geographic coverage

National Coverage

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most low- and middle-income economies, Global Findex data were collected through face-to-face interviews. In these economies, an area frame design was used for interviewing. In most high-income economies, telephone surveys were used. In 2024, face-to-face interviews were again conducted in 22 economies after phone-based surveys had been employed in 2021 as a result of mobility restrictions related to COVID-19. In addition, an abridged form of the questionnaire was administered by phone to survey participants in Algeria, China, the Islamic Republic of Iran, Libya, Mauritius, and Ukraine because of economy-specific restrictions. In just one economy, Singapore, did the interviewing mode change from face to face in 2021 to phone based in 2024.

In economies in which face-to-face surveys were conducted, the first stage of sampling was the identification of primary sampling units. These units were then stratified by population size, geography, or both and clustered through one or more stages of sampling. Where population information was available, sample selection was based on probabilities proportional to population size; otherwise, simple random sampling was used. Random route procedures were used to select sampled households. Unless an outright refusal occurred, interviewers made up to three attempts to survey each sampled household. To increase the probability of contact and completion, attempts were made at different times of the day and, where possible, on different days. If an interview could not be completed at a household that was initially part of the sample, a simple substitution method was used to select a replacement household for inclusion.

Respondents were randomly selected within sampled households. Each eligible household member (that is, all those ages 15 or older) was listed, and a handheld survey device randomly selected the household member to be interviewed. For paper surveys, the Kish grid method was used to select the respondent. In economies in which cultural restrictions dictated gender matching, respondents were randomly selected from among all eligible adults of the interviewer’s gender.

In economies in which Global Findex surveys have traditionally been phone based, respondent selection followed the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies in which mobile phone and landline penetration is high, a dual sampling frame was used.

The same procedure for respondent selection was applied to economies in which phone-based interviews were being conducted for the first time. Dual-frame (landline and mobile phone) random digit dialing was used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digit dialing was used in economies with limited or no landline presence (less than 20 percent). For landline respondents in economies in which mobile phone or landline penetration is 80 percent or higher, respondents were selected randomly by using either the next-birthday method or the household enumeration method, which involves listing all eligible household members and randomly selecting one to participate. For mobile phone respondents in these economies or in economies in which mobile phone or landline penetration is less than 80 percent, no further selection was performed. At least three attempts were made to reach the randomly selected person in each household, spread over different days and times of day.

Research instrument

The English version of the questionnaire is provided for download.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in: Klapper, Leora, Dorothe Singer, Laura Starita, and Alexandra Norris. 2025. The Global Findex Database 2025: Connectivity and Financial Inclusion in the Digital Economy. Washington, DC: World Bank. https://doi.org/10.1596/978-1-4648-2204-9.

Abstract

Well-functioning financial systems serve a vital purpose, offering savings, credit, payment, and risk management products to people with a wide range of needs. Yet until now little had been known about the global reach of the financial sector - the extent of financial inclusion and the degree to which such groups as the poor, women, and youth are excluded from formal financial systems. Systematic indicators of the use of different financial services had been lacking for most economies.

The Global Financial Inclusion (Global Findex) database provides such indicators. This database contains the first round of Global Findex indicators, measuring how adults in more than 140 economies save, borrow, make payments, and manage risk. The data set can be used to track the effects of financial inclusion policies globally and develop a deeper and more nuanced understanding of how people around the world manage their day-to-day finances. By making it possible to identify segments of the population excluded from the formal financial sector, the data can help policy makers prioritize reforms and design new policies.

Geographic coverage

National Coverage.

Analysis unit

Individual

Universe

The target population is the civilian, non-institutionalized population 15 years and above. The sample is nationally representative.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Global Findex indicators are drawn from survey data collected by Gallup, Inc. over the 2011 calendar year, covering more than 150,000 adults in 148 economies and representing about 97 percent of the world's population. Since 2005, Gallup has surveyed adults annually around the world, using a uniform methodology and randomly selected, nationally representative samples. The second round of Global Findex indicators was collected in 2014 and is forthcoming in 2015. The set of indicators will be collected again in 2017.

Surveys were conducted face-to-face in economies where landline telephone penetration is less than 80 percent, or where face-to-face interviewing is customary. The first stage of sampling is the identification of primary sampling units, consisting of clusters of households. The primary sampling units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households by means of the Kish grid.

Surveys were conducted by telephone in economies where landline telephone penetration is over 80 percent. The telephone surveys were conducted using random digit dialing or a nationally representative list of phone numbers. In selected countries where cell phone penetration is high, a dual sampling frame is used. Random respondent selection is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to teach a person in each household, spread over different days and times of year.

The sample size in Cameroon was 1,000 individuals.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup, Inc. also provided valuable input. The questionnaire was piloted in over 20 countries using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on insurance, mobile payments, and loan purposes were asked only in developing economies. The indicators on awareness and use of microfinance insitutions (MFIs) are not included in the public dataset. However, adults who report saving at an MFI are considered to have an account; this is reflected in the composite account indicator.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country- and indicator-specific standard errors, refer to the Annex and Country Table in Demirguc-Kunt, Asli and L. Klapper. 2012. "Measuring Financial Inclusion: The Global Findex." Policy Research Working Paper 6025, World Bank, Washington, D.C.

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.

Abstract

The Global Findex 2025 reveals how mobile technology is equipping more adults around the world to own and use financial accounts to save formally, access credit, make and receive digital payments, and pursue opportunities. Including the inaugural Global Findex Digital Connectivity Tracker, this fifth edition of Global Findex presents new insights on the interactions among mobile phone ownership, internet use, and financial inclusion.

The Global Findex is the world’s most comprehensive database on digital and financial inclusion. It is also the only global source of comparable demand-side data, allowing cross-country analysis of how adults access and use mobile phones, the internet, and financial accounts to reach digital information and resources, save, borrow, make payments, and manage their financial health. Data for the Global Findex 2025 were collected from nationally representative surveys of about 145,000 adults in 141 economies. The latest edition follows the 2011, 2014, 2017, and 2021 editions and includes new series measuring mobile phone ownership and internet use, digital safety, and frequency of transactions using financial services.

The Global Findex 2025 is an indispensable resource for policy makers in the fields of digital connectivity and financial inclusion, as well as for practitioners, researchers, and development professionals.

Geographic coverage

National Coverage

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most low- and middle-income economies, Global Findex data were collected through face-to-face interviews. In these economies, an area frame design was used for interviewing. In most high-income economies, telephone surveys were used. In 2024, face-to-face interviews were again conducted in 22 economies after phone-based surveys had been employed in 2021 as a result of mobility restrictions related to COVID-19. In addition, an abridged form of the questionnaire was administered by phone to survey participants in Algeria, China, the Islamic Republic of Iran, Libya, Mauritius, and Ukraine because of economy-specific restrictions. In just one economy, Singapore, did the interviewing mode change from face to face in 2021 to phone based in 2024.

In economies in which face-to-face surveys were conducted, the first stage of sampling was the identification of primary sampling units. These units were then stratified by population size, geography, or both and clustered through one or more stages of sampling. Where population information was available, sample selection was based on probabilities proportional to population size; otherwise, simple random sampling was used. Random route procedures were used to select sampled households. Unless an outright refusal occurred, interviewers made up to three attempts to survey each sampled household. To increase the probability of contact and completion, attempts were made at different times of the day and, where possible, on different days. If an interview could not be completed at a household that was initially part of the sample, a simple substitution method was used to select a replacement household for inclusion.

Respondents were randomly selected within sampled households. Each eligible household member (that is, all those ages 15 or older) was listed, and a handheld survey device randomly selected the household member to be interviewed. For paper surveys, the Kish grid method was used to select the respondent. In economies in which cultural restrictions dictated gender matching, respondents were randomly selected from among all eligible adults of the interviewer’s gender.

In economies in which Global Findex surveys have traditionally been phone based, respondent selection followed the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies in which mobile phone and landline penetration is high, a dual sampling frame was used.

The same procedure for respondent selection was applied to economies in which phone-based interviews were being conducted for the first time. Dual-frame (landline and mobile phone) random digit dialing was used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digit dialing was used in economies with limited or no landline presence (less than 20 percent). For landline respondents in economies in which mobile phone or landline penetration is 80 percent or higher, respondents were selected randomly by using either the next-birthday method or the household enumeration method, which involves listing all eligible household members and randomly selecting one to participate. For mobile phone respondents in these economies or in economies in which mobile phone or landline penetration is less than 80 percent, no further selection was performed. At least three attempts were made to reach the randomly selected person in each household, spread over different days and times of day.

Research instrument

The English version of the questionnaire is provided for download.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in: Klapper, Leora, Dorothe Singer, Laura Starita, and Alexandra Norris. 2025. The Global Findex Database 2025: Connectivity and Financial Inclusion in the Digital Economy. Washington, DC: World Bank. https://doi.org/10.1596/978-1-4648-2204-9.