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

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).

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.

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.

The 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.

The 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.

DESCRIPTION: This dataset provides comprehensive information about various smartphone models, facilitating detailed analysis and comparison. It encompasses crucial attributes such as model name, price, rating, similarity index (sim), processor details, RAM capacity, battery specifications, display features, camera specifications, expandable memory card support, and operating system (OS) details.

Columns:

1.Model: The name or designation of the smartphone model.

2.Price: The retail price of the smartphone, typically in a specified currency.

3.Rating: The user or expert rating of the smartphone, providing an indication of its overall quality and user satisfaction.

4.Sim: The number of SIM cards supported by the smartphone.

5.Processor: Details about the processor or chipset used in the smartphone, including brand, model, and specifications.

6.RAM: The amount of random-access memory (RAM) available in the smartphone, measured in gigabytes (GB).

7.Battery: Information regarding the battery capacity and type, providing insight into the device's endurance and usage duration.

8.Display: Specifications related to the smartphone's display, such as size, resolution, technology (e.g., LCD, OLED), and any additional features (e.g., HDR support).

9.Camera: Details about the smartphone's camera setup, including megapixel count, lens specifications, and additional features like image stabilization or AI enhancements.

10.Card: Indicates whether the smartphone supports expandable memory cards, providing users with the option to increase storage capacity.

11.OS: The operating system installed on the smartphone, including the version number if applicable.

This dataset is valuable for various analyses, including:

1.Price-performance comparisons: Assessing the correlation between price and features like processor, RAM, and camera quality.

2.Market trend analysis: Identifying popular models based on ratings and sales data.

3.Feature preference analysis: Examining which features (e.g., battery capacity, camera quality) are most valued by consumers.

4.Brand comparison: Comparing specifications and performance across different smartphone brands.

5.Predictive modeling: Using historical data to predict future trends in smartphone design and consumer preferences.

The dataset is ideal for data scientists, researchers, and analysts interested in the smartphone industry, consumer behavior, and technology trends. It can be utilized for exploratory data analysis, machine learning modeling, and generating insights to inform business decisions within the mobile device market.

Apple is one of the most influential and recognisable brands in the world, responsible for the rise of the smartphone with the iPhone. Valued at over $2 trillion in 2021, it is also the most valuable...

This dataset was collected from the Smartphone sensors and can be used to analyse behaviour of a crowd, for example, an anomaly.

Dataset Characteristics: Time-Series

Subject Area: Computer Science

Associated Tasks: Classification

Instances: 14221

Dataset Information

For what purpose was the dataset created?

The key purpose of donating this dataset is to provide an opportunity to the research community to use it for further research purposes.

Who funded the creation of the dataset? Muhammad Irfan

What do the instances in this dataset represent? One instance represents a movement patter for a group based activity.

Are there recommended data splits? No.

Has Missing Values? No

Introductory Paper

Title: Anomaly Detection in Crowds using Multi Sensory Information

Author:M. Irfan, L. Marcenaro, and L. Tokarchuk, C. Regazzoni. 2018

Journal: Published in 5th IEEE International Conference on Advanced Video and Signal-based Surveillance (AVSS), Auckland, New Zealand,

Link: https://ieeexplore.ieee.org/document/8639151

Abstract of Introductory Paper

This paper presents, a system capable of detecting unusual activities in crowds from real-world data captured from multiple sensors. The detection is achieved by classifying the distinct movements of people in crowds, and those patterns can be different and can be classified as normal and abnormal activities. Statistical features are extracted from the dataset collected by applying sliding time window operations. A model for classifying movements is trained by using Random Forest technique. The system was tested by using two datasets collected from mobile phones during social events gathering. Results show that mobile data can be used to detect anomalies in crowds as an alternative to video sensors with significant performances. Our approach is the first to detect any unusual behavior in crowd with non-visual data, which is simple to train and easy to deploy. We also present our dataset for public research as there is no such dataset available to perform experiments on crowds for detecting unusual behaviours.

Cite

Citation: Irfan,Muhammad. (2021). Smartphone Dataset for Anomaly Detection in Crowds. UCI Machine Learning Repository. https://doi.org/10.24432/C5Q90H.

BibTeX: @misc{misc_smartphone_dataset_for_anomaly_detection_in_crowds_613, author = {Irfan,Muhammad}, title = {{Smartphone Dataset for Anomaly Detection in Crowds}}, year = {2021}, howpublished = {UCI Machine Learning Repository}, note = {{DOI}: https://doi.org/10.24432/C5Q90H} }

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.

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.

The number of smartphone users in Ireland was forecast to continuously increase between 2024 and 2029 by in total 0.3 million users (+6.15 percent). After the seventh consecutive increasing year, the smartphone user base is estimated to reach 5.22 million users and therefore a new peak in 2029. 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 information concerning Serbia and Sweden.

Context, Sources & Inspiration:

This dataset was created to provide a comprehensive overview of the global smartphone market in 2025. The dataset contains over 1000 mobile phone models from popular brands including Apple, Samsung, Xiaomi, Oppo, Vivo, Google, OnePlus, Realme, and Infinix.

Sources & Methodology:

Data was compiled using publicly available specifications from brand websites, tech blogs, and market reports.

Prices are in USD and represent typical retail pricing for 2025.

Technical specifications such as RAM, storage, camera, battery, display size, processor, and OS were collected from official manufacturer sources and verified against multiple tech review sites.

Ratings are simulated realistic averages (3.5–5.0) to provide analysis opportunities.

Inspiration:

The goal is to provide an easy-to-use dataset for ML projects, price prediction, market trend visualization, and exploratory data analysis (EDA).

Designed for students, analysts, and ML enthusiasts who want a complete and realistic dataset for smartphones.

📊 About the Dataset

Technology adoption has been evolving rapidly, shaping industries and consumer behaviors worldwide. This dataset provides insights into global gadget consumption trends from 2015 to 2025, covering smartphones, laptops, gaming consoles, smartwatches, and 5G penetration rates.

🎯 Use Cases

• 📈 Market research & consumer behavior analysis
• 🔮 Forecasting future tech adoption trends
• ♻️ Studying the impact of e-waste generation
• 🌐 Understanding 5G penetration across different countries

📑 Column Descriptions

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.

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).

An Approach to Optimize Device Power Performance Towards Energy Efficient Next Generation 5G Networks

A Thermal Aware Approach to Enhance 5G Device Performance and Reliability in mmWave Networks

If you use this dataset and code or any herein modified part of it in any publication, please cite the papers:

A. Thantharate, C. Beard and S. Marupaduga, "An Approach to Optimize Device Power Performance Towards Energy Efficient Next Generation 5G Networks," 2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), 2019, pp. 0749-0754, doi: 10.1109/UEMCON47517.2019.8993067.

A. Thantharate, C. Beard and S. Marupaduga, "A Thermal Aware Approach to Enhance 5G Device Performance and Reliability in mmWave Networks," 2020 International Symposium on Networks, Computers and Communications (ISNCC), 2020, pp. 1-5, doi: 10.1109/ISNCC49221.2020.9297313.

Please reach out adtmv7@umkc.edu if you have any additional questions.

An Approach to Optimize Device Power Performance Towards Energy Efficient Next Generation 5G Networks https://ieeexplore.ieee.org/abstract/document/8993067/

In Fifth Generation (5G), wireless cellular networks, smartphone battery efficiency, and optimal utilization of power have become a matter of utmost importance. Battery and power are an area of significant challenges considering smartphones these days are equipped with advanced technological network features and systems. These features require much simultaneous power to make decisions and to transfer information between devices and network to provide best the user experience. Furthermore, to meet the demands of increased data capacity, data rate, and to provide the best quality of service, there is a need to adopt energy-efficient architectures. This paper presents system-level architectural changes on both User Equipment (UE) and Network elements along with a proposal to modify control signaling as part of Radio Resource Control messages using smartphone battery level. Additionally, we presented real-world 5G mmWave field results, showing impacts on device battery life in varying RF conditions and proposed methods to allocate optimal network resources and improve the energy efficiency by modifying radio layer parameters between devices and base stations. Without these proposed architecture level and system-level algorithm changes, realizing optimal and consistent 5G speeds will be near impossible.

**A Thermal Aware Approach to Enhance 5G Device Performance and Reliability in mmWave Networks https://ieeexplore.ieee.org/abstract/document/9297313 ** 5G NR (New Radio) mmWave networks are creating novel avenues of numerous possibilities and improving mobile broadband in terms of capacity, throughput, and performance, driven by the insatiable demand for faster and better user experience. However, one of the critical problem areas for User Equipment (UE) in mmWave networks is the fast depletion of UE battery power, increase in thermal levels caused by limited coverage and lot of overhead signaling due to rapid radio frequency (RF) and environment changes. With the growing inclusion of advanced functionality on mobile devices, power consumption is growing in parallel, which causes devices to increase thermal temperature, causing an impact on overall system performance. This paper presents system-level change proposal on control signaling between UE and network elements along with changes in UE thermal algorithms based on device battery levels and the coverage of the 5G mmWave networks to deliver the best device performance and user experience. Furthermore, we present real-world field results captured on mmWave networks showing impacts on UE performance with respect to thermal generation in different RF conditions. Our proposal will allocate optimal network resources by modifying the system selection on both UE and base stations. Without the proposed model, realizing the benefits of the 5G NR system along with achieving seamless cellular user experience would be near impossible.

5G , NR , LTE , mmWave , Smartphone , Battery , Power Optimization , Energy Efficiency , Network Efficiency, 5G NR , mmWave , Smartphone , Thermal , Device Temperature , User Equipment , Battery , Network Efficiency , Load Balancing , 3GPP , Power Efficiency , Green Energy

Context

Sourced directly from the popular price comparison platform Smartprix, this dataset offers a raw and dynamic look into the mobile phone market. It is updated daily, ensuring you have access to the latest device information. The dataset is intentionally left in its "uncleaned" state, presenting a fantastic real-world challenge for data enthusiasts looking to practice their data wrangling, preprocessing, and feature engineering skills.

Content

The data is provided in a single CSV file, mobiles.csv. Each row corresponds to a specific mobile phone model. A key characteristic of this dataset is its hierarchical column structure, which uses a dot notation (e.g., Category.Feature) to represent nested attributes. This format reflects the raw data as it was collected and requires parsing for effective analysis.

The dataset is comprehensive, covering a wide range of mobile phone specifications, including:

• General: Brand, Model, OS, Release Date, Sim Type
• Design: Dimensions, Weight, Colors, Build Material
• Display: Size, Type, Resolution, Refresh Rate, PPI, Screen to Body Ratio
• Camera: Rear and Front Camera specifications (megapixels, features, video recording)
• Battery: Capacity, Fast Charging support, Type
• Connectivity: 5G/4G support, Bluetooth, Wi-Fi, USB, GPS
• Performance: Processor details, RAM, Internal Storage
• Pricing: Current Price

Key characteristics of this dataset:

• Raw and Uncleaned: The data contains missing values, requires data type conversions, and has inconsistencies that need to be handled before analysis.
• Dynamic: Reflecting the fast-paced mobile market, this dataset is updated daily.
• Hierarchical Columns: The dot-separated column names (Display.Size, Camera.Features, etc.) will challenge you to parse and flatten the data into a usable format for machine learning.

Potential Uses & Inspiration

This dataset is a perfect playground for various data science projects:

• Data Cleaning and Preprocessing: Master the art of preparing messy, real-world data for analysis.
• Exploratory Data Analysis (EDA): Discover trends in the mobile phone industry. Which features are becoming standard? How do camera specs correlate with price? What are the most common battery capacities?
• Price Prediction: Develop a model to predict the price of a mobile phone based on its features.
• Feature Engineering: Create new, insightful features from the existing raw data to improve your model's performance.

Dive in and explore the ever-evolving world of mobile technology!