Dataset

This dataset was created by Michael Lomuscio

Contents

Android maintained its position as the leading mobile operating system worldwide in the third quarter of 2025 with a market share of about ***** percent. Android's closest rival, Apple's iOS, had a market share of approximately ***** percent during the same period. The leading mobile operating systems Both unveiled in 2007, Google’s Android and Apple’s iOS have evolved through incremental updates introducing new features and capabilities. The latest version of iOS, iOS 18, was released in September 2024, while the most recent Android iteration, Android 15, was made available in September 2023. A key difference between the two systems concerns hardware - iOS is only available on Apple devices, whereas Android ships with devices from a range of manufacturers such as Samsung, Google and OnePlus. In addition, Apple has had far greater success in bringing its users up to date. As of February 2024, ** percent of iOS users had iOS 17 installed, while in the same month only ** percent of Android users ran the latest version. The rise of the smartphone From around 2010, the touchscreen smartphone revolution had a major impact on sales of basic feature phones, as the sales of smartphones increased from *** million units in 2008 to **** billion units in 2023. In 2020, smartphone sales decreased to **** billion units due to the coronavirus (COVID-19) pandemic. Apple, Samsung, and lately also Xiaomi, were the big winners in this shift towards smartphones, with BlackBerry and Nokia among those unable to capitalize.

This dataset simulates anonymized mobile screen time and app usage data collected from Android/iOS users over a 3-month period (Jan–April 2024). It captures daily usage trends across various app categories including:

Productivity: Google Docs, Notion, Slack

Entertainment: YouTube, Netflix, TikTok

Social Media: Instagram, WhatsApp, Facebook

Utilities: Chrome, Gmail, Maps

For YouTube, additional engagement statistics such as views, likes, and comments are included to analyze video popularity and content consumption behavior.

The dataset enables exploration of:

Productivity vs. entertainment screen time patterns

Daily usage fluctuations

App-specific user engagement

Correlation between time spent and user interactions

YouTube content virality metrics

This is a great resource for:

EDA projects

Behavioral clustering

Dashboard development

Time series and anomaly detection

Building recommendation or focus-assistive apps

Mobile App Statistics (Apple iOS app store)

The ever-changing mobile landscape is a challenging space to navigate. . The percentage of mobile over desktop is only increasing. Android holds about 53.2% of the smartphone market, while iOS is 43%. To get more people to download your app, you need to make sure they can easily find your app. Mobile app analytics is a great way to understand the existing strategy to drive growth and retention of future user.

With million of apps around nowadays, the following data set has become very key to getting top trending apps in iOS app store. This data set contains more than 7000 Apple iOS mobile application details. The data was extracted from the iTunes Search API at the Apple Inc website. R and linux web scraping tools were used for this study.

Interactive full Shiny app can be seen here( https://multiscal.shinyapps.io/appStore/)

Data collection date (from API); July 2017

Dimension of the data set; 7197 rows and 16 columns

Content:

appleStore.csv

1. "id" : App ID

2. "track_name": App Name

3. "size_bytes": Size (in Bytes)

4. "currency": Currency Type

5. "price": Price amount

6. "rating_count_tot": User Rating counts (for all version)

7. "rating_count_ver": User Rating counts (for current version)

8. "user_rating" : Average User Rating value (for all version)

9. "user_rating_ver": Average User Rating value (for current version)

10. "ver" : Latest version code

11. "cont_rating": Content Rating

12. "prime_genre": Primary Genre

13. "sup_devices.num": Number of supporting devices

14. "ipadSc_urls.num": Number of screenshots showed for display

15. "lang.num": Number of supported languages

16. "vpp_lic": Vpp Device Based Licensing Enabled

appleStore_description.csv

1. id : App ID
2. track_name: Application name
3. size_bytes: Memory size (in Bytes)
4. app_desc: Application description

Acknowledgements

The data was extracted from the iTunes Search API at the Apple Inc website. R and linux web scraping tools were used for this study.

Inspiration

1. How does the App details contribute the user ratings?
2. Try to compare app statistics for different groups?

Reference: R package From github, with devtools::install_github("ramamet/applestoreR")

Licence

Copyright (c) 2018 Ramanathan Perumal

About Dataset

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

Description

The dataset includes the following key features:

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

Detail

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

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

Usage

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

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

Summary

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

Social media companies are starting to offer users the option to subscribe to their platforms in exchange for monthly fees. Until recently, social media has been predominantly free to use, with tech companies relying on advertising as their main revenue generator. However, advertising revenues have been dropping following the COVID-induced boom. As of July 2023, Meta Verified is the most costly of the subscription services, setting users back almost 15 U.S. dollars per month on iOS or Android. Twitter Blue costs between eight and 11 U.S. dollars per month and ensures users will receive the blue check mark, and have the ability to edit tweets and have NFT profile pictures. Snapchat+, drawing in four million users as of the second quarter of 2023, boasts a Story re-watch function, custom app icons, and a Snapchat+ badge.

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.

Key Features Included

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.

Usage & Applications

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.).

Important Notes & Disclaimer

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.

Mobile Phones Dataset Description

This dataset provides comprehensive information about various mobile phones, including their specifications and features. The dataset includes the following columns:

Brand and Model Information

• brand_name: The brand name of the mobile phone manufacturer.
• model: The specific model name or designation of the mobile phone.

General Specifications

• price: The price of the mobile phone in the respective currency.
• avg_rating: The average rating or score given by users or reviewers for the mobile phone.
• 5G_or_not: Indicates whether the mobile phone supports 5G connectivity (0 for no, 1 for yes).

Processor Details

• processor_brand: The brand or manufacturer of the processor used in the mobile phone.
• num_cores: The number of cores in the processor.
• processor_speed: The speed or clock rate of the processor, typically measured in GHz.

Battery and Charging

• battery_capacity: The capacity of the mobile phone's battery, typically measured in mAh.
• fast_charging_available: Indicates whether the mobile phone supports fast charging (0 for no, 1 for yes).
• fast_charging: The fast charging capability or technology used by the mobile phone (e.g., Quick Charge, Dash Charge, etc.).

Memory and Storage

• ram_capacity: The amount of RAM (Random Access Memory) available in the mobile phone, typically measured in GB.
• internal_memory: The internal storage capacity of the mobile phone, typically measured in GB.
• screen_size: The size of the mobile phone's display screen, typically measured in inches.
• refresh_rate: The refresh rate of the display screen, typically measured in Hz.
• extended_memory_available: Indicates whether the mobile phone supports expandable memory via a memory card slot (0 for no, 1 for yes).

Camera

• num_rear_cameras: The number of rear-facing cameras on the mobile phone.
• primary_camera_rear: The resolution or megapixel count of the primary rear-facing camera.
• primary_camera_front: The resolution or megapixel count of the primary front-facing (selfie) camera.

Display

• resolution_height: The height dimension of the display screen resolution, typically measured in pixels.
• resolution_width: The width dimension of the display screen resolution, typically measured in pixels.

Operating System

• os: The operating system installed on the mobile phone (e.g., Android, iOS, etc.).

The dataset provides a comprehensive overview of various mobile phone models, allowing for analysis and comparison of their specifications and features.

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This dataset, titled "Phone Listings from GSMArena.com," consists of two primary files: data.json and processed_data.csv, each containing detailed information about various phone models available on the market.

data.json File This file holds the raw, unprocessed data scraped from GSMArena.com. The columns and attributes include:

phone_brand: The brand or manufacturer of the phone (e.g., Apple, Samsung, Xiaomi). phone_model: The specific model or number of the phone. price: The price point of the phone, which can either be an exact figure or a rough estimate. This column might require data cleaning due to inconsistencies. specs: A nested dictionary that details the phone’s technical specifications. This includes features such as screen size, camera resolution, processor type, battery life, and other relevant hardware components. pricing: A nested dictionary containing price listings for the phone across various e-commerce platforms. processed_data.csv File This file contains cleaned and processed phone data, aggregated from various e-commerce sources. The columns are more refined, and each phone entry provides comprehensive details:

phone_brand: The manufacturer or brand of the phone. phone_model: The specific model or name of the phone. store: The particular store or e-commerce platform where the phone is listed. price: The price of the phone as a floating-point number, set in the native currency. currency: The currency in which the phone is priced (e.g., USD, EUR). price_USD: The phone price converted into USD. storage: The storage capacity of the phone, measured in gigabytes (GB). ram: The amount of RAM available in the phone, also measured in gigabytes (GB). Launch: The official launch date of the phone, represented in a datetime format. Dimensions: The physical dimensions of the phone, typically provided in millimeters (e.g., 163.8 x 76.8 x 8.9 mm). Weight: The weight of the phone, measured in grams. Display_Type: The type of display technology used, for example, "LTPO Super Retina XDR OLED, 120Hz, HDR10." Display_Size: The size of the phone's display in inches. Display_Resolution: The resolution of the phone's display (e.g., 1280 x 2856 pixels). OS: The phone's operating system, such as iOS 18 or Android 14. NFC: A flag indicating the presence of Near Field Communication (NFC), with values of 1 for phones that have NFC and 0 for phones that do not. USB: The type of USB port (e.g., USB Type-C 3.2 Gen 2). BATTERY: The battery capacity of the phone, measured in milliampere hours (mAh). Features_Sensors: Various features and sensors included with the phone (e.g., fingerprint scanner, accelerometer). Colors: Available color options for the phone model (e.g., Black Titanium, White Titanium). Video: Camera specifications for video recording, including supported resolutions and frame rates (e.g., 4K@30fps). Chipset: The chipset model in the phone, such as "Apple A18 Pro (3 nm)." CPU: Specifications of the central processing unit (CPU) (e.g., Hexa-core, 2x4.05 GHz). GPU: Specifications of the graphical processing unit (GPU). Year: The year in which the phone model was released. Foldable: A flag indicating whether the phone is foldable (1 = foldable, 0 = not foldable). PPI_Density: The pixel density of the display in pixels per inch (ppi). quantile_10, quantile_50, quantile_90: These columns represent the 10th, 50th (median), and 90th quantiles of phone prices in a given year. price_range: This column classifies phones into different price ranges (low, medium, or high), based on their position in the price distribution (quantiles). Overall, this dataset provides extensive information on phone models, offering both raw and processed views of phone listings, along with important price and technical details.

This dataset contains 30 countries with the level of their users proficiency with digital devices.

Country: Country DLRate: Digital Literacy Rate Population (est): Population by country (in number) Smart Phone Users: Smart phone users by country (in number) Android Users: In percent
iOS Users: In percent Windows Users: In percent
PC Users: In percent
Age Bound: Users age bracket Source 1 Source 2 Source 3: Valid information sources, from where the data was gathered

To get more information, reach me on https://github.com/samzing777

The dataset appears to focus on a collection of fitness applications, providing detailed information about various features, user ratings, and feedback. The columns include:

App Name: The name of the fitness application. User Rating (out of 5): The average rating given by users. OS Supported: The operating systems the app supports (e.g., iOS, Android). Key Features: A list of notable features for each app (e.g., calorie counting, workout plans). No. of Downloads (in millions): The total number of times the app has been downloaded. In-App Purchases: Indicates whether the app offers in-app purchases. Subscription Model: Information about the app's subscription options (monthly, yearly, or free). User Satisfaction (%): The percentage of users who are satisfied with the app. Common User Feedback: General user comments and feedback about the app (e.g., easy to use, accurate tracking). Positive Feedback: Specific positive aspects highlighted by users. Negative Feedback: Common criticisms or drawbacks users have encountered. This dataset provides a comprehensive comparison of popular fitness apps, allowing for an analysis of their popularity, features, pricing models, and user experiences. It includes both qualitative data (e.g., user feedback) and quantitative data (e.g., downloads, ratings, user satisfaction).

About Dataset

The "Time-Wasters on Social Media" dataset provides a comprehensive insight into user interactions and engagement with various social media platforms. This dataset encompasses a wide range of attributes that facilitate a thorough analysis of how social media affects users' time management and productivity. It serves as an essential resource for researchers, marketers, and social scientists who seek to delve into the intricacies of social media consumption patterns.

Generated through advanced synthetic data techniques using tools like NumPy and pandas, this dataset mimics real-world social media usage scenarios. Despite being artificially created, it accurately reflects genuine usage trends, making it a valuable asset for conducting research and analysis in the realm of social media behavior.

Columns Description

• UserID: Unique identifier assigned to each user.
• Age: The user's age. - Gender: The user's gender (e.g., male, female, non-binary).
• Location: Geographic location of the user.
• Income: The user's income level.
• Debt: Amount of debt the user has.
• Owns Property: Indicates whether the user owns property.
• Profession: The user's occupation or job.
• Demographics: Statistical data about the user (e.g., age, gender, income).
• Platform: The platform the user is using (e.g., website, mobile app).
• Total Time Spent: The total time the user spends on the platform.
• Number of Sessions: The number of times the user has logged into the platform.
• Video ID: Unique identifier for a video.
• Video Category: The category or genre of the video.
• Video Length: Duration of the video.
• Engagement: User interaction with the video (e.g., likes, comments, shares).
• Importance Score: A score indicating how important the video is to the user.
• Time Spent On Video: The amount of time the user spends watching a video.
• Number of Videos Watched: The total number of videos watched by the user.
• Scroll Rate: The rate at which the user scrolls through content.
• Frequency: How often the user engages with the platform.
• Productivity Loss: The impact of platform usage on the user's productivity.
• Satisfaction: The user's satisfaction level with the platform or content.
• Watch Reason: The reason why the user is watching a video (e.g., entertainment, education).
• Device Type: The type of device the user is using (e.g., smartphone, tablet, desktop).
• OS: The operating system of the user's device (e.g., iOS, Android, Windows).
• Watch Time: The time of day when the user watches videos.
• Self Control: The user's ability to control their usage of the platform.
• Addiction Level: The user's level of dependency on the platform.
• Current Activity: What the user is doing while watching the video.
• Connection Type: The type of internet connection the user has (e.g., Wi-Fi, cellular).

Sources: Kaggle This dataset, created using NumPy and Pandas, mimics real-world social media usage patterns for research and analysis through synthetic data generation techniques.

Collection Methodology

About Dataset The "Time-Wasters on Social Media" dataset provides a comprehensive insight into user interactions and engagement with various social media platforms. This dataset encompasses a wide range of attributes that facilitate a thorough analysis of how social media affects users' time management and productivity. It serves as an essential resource for researchers, marketers, and social scientists who seek to delve into the intricacies of social media consumption patterns.

Generated through advanced synthetic data techniques using tools like NumPy and pandas, this dataset mimics real-world social media usage scenarios. Despite being artificially created, it accurately reflects genuine usage trends, making it a valuable asset for conducting research and analysis in the realm of social media behavior.

Columns Description UserID: Unique identifier assigned to each user. Age: The user's age. - Gender: The user's gender (e.g., male, female, non-binary). Location: Geographic location of the user. Income: The user's income level. Debt: Amount of debt the user has. Owns Property: Indicates whether the user owns property. Profession: The user's occupation or job. Demographics: Statistical data about the user (e.g., age, gender, income). Platform: The platform the user is using (e.g., website, mobile app). Total Time Spent: The total time the user spends on the platform. Number of Sessions: The number of times the user has logged into the platform. Video ID: Unique identifier for a video. Video Category: The category or genre of the video. Video Length: Duration of the video. Engagement: User interaction with the video (e.g., likes, comments, shares). Importance Score: A score indicating how important the video is to the user. Time Spent On Video: The amount of time the user spends watching a video. Number of Videos Watched: The total number of videos watched by the user. Scroll Rate: The rate at which the user scrolls through content. Frequency: How often the user engages with the platform. Productivity Loss: The impact of platform usage on the user's productivity. Satisfaction: The user's satisfaction level with the platform or content. Watch Reason: The reason why the user is watching a video (e.g., entertainment, education). Device Type: The type of device the user is using (e.g., smartphone, tablet, desktop). OS: The operating system of the user's device (e.g., iOS, Android, Windows). Watch Time: The time of day when the user watches videos. Self Control: The user's ability to control their usage of the platform. Addiction Level: The user's level of dependency on the platform. Current Activity: What the user is doing while watching the video. Connection Type: The type of internet connection the user has (e.g., Wi-Fi, cellular).

The number of mobile broadband connections in the Philippines was forecast to continuously increase between 2024 and 2029 by in total 18.3 million connections (+20.46 percent). After the ninth consecutive increasing year, the number of connections is estimated to reach 107.69 million connections and therefore a new peak in 2029. Mobile broadband connections include cellular connections with a download speed of at least 256 kbit/s (without satellite or fixed-wireless connections). Cellular Internet-of-Things (IoT) or machine-to-machine (M2M) connections are excluded. 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 mobile broadband connections in countries like Vietnam and Laos.