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.

Involves a crawler to collect data from the Google Play store including the application's metadata and APK files. The manifest files were extracted from the APK files and then processed to extract the features. The data set is composed of 870,515 records/apps, and for each app we produced 48 features. The data set was used to built and test two bootstrap aggregating of multiple XGBoost machine learning classifiers. The dataset were collected between April 2017 and November 2018. We then checked the status of these applications on three different occasions; December 2018, February 2019, and May-June 2019. (2022-06-03)

[ADVISORY] IMPORTANT

Instructions for citation:

If you use this dataset anywhere in your work, kindly cite as the below: L. Gupta, "Google Play Store Apps," Feb 2019. [Online]. Available: https://www.kaggle.com/lava18/google-play-store-apps

Context

While many public datasets (on Kaggle and the like) provide Apple App Store data, there are not many counterpart datasets available for Google Play Store apps anywhere on the web. On digging deeper, I found out that iTunes App Store page deploys a nicely indexed appendix-like structure to allow for simple and easy web scraping. On the other hand, Google Play Store uses sophisticated modern-day techniques (like dynamic page load) using JQuery making scraping more challenging.

Content

Each app (row) has values for catergory, rating, size, and more.

Acknowledgements

This information is scraped from the Google Play Store. This app information would not be available without it.

Inspiration

The Play Store apps data has enormous potential to drive app-making businesses to success. Actionable insights can be drawn for developers to work on and capture the Android market!

This dataset contains fictional reviews from a hypothetical mobile application, generated for demo purposes in various projects. The reviews include detailed feedback from users across different countries and platforms, with additional attributes such as star ratings, like/dislike counts, and issue flags. The data was later used as an input for a large language model (LLM) to generate labeled outputs, which are included in a separate dataset named labeled_app_store_reviews. This labeled dataset can be used for machine learning tasks such as sentiment analysis, text classification, or even A/B testing simulations.

Aaditya1/Mobile-Application-Data dataset hosted on Hugging Face and contributed by the HF Datasets community

This dataset offers a focused and invaluable window into user perceptions and experiences with applications listed on the Apple App Store. It is a vital resource for app developers, product managers, market analysts, and anyone seeking to understand the direct voice of the customer in the dynamic mobile app ecosystem.

Dataset Specifications:

• Investment: $45.0
• Status: Published and immediately available.
• Category: Ratings and Reviews Data
• Format: Compressed ZIP archive containing JSON files, ensuring easy integration into your analytical tools and platforms.
• Volume: Comprises 10,000 unique app reviews, providing a robust sample for qualitative and quantitative analysis of user feedback.
• Timeliness: Last crawled: (This field is blank in your provided info, which means its recency is currently unknown. If this were a real product, specifying this would be critical for its value proposition.)

Richness of Detail (11 Comprehensive Fields):

Each record in this dataset provides a detailed breakdown of a single App Store review, enabling multi-dimensional analysis:

1. Review Content:

   • review: The full text of the user's written feedback, crucial for Natural Language Processing (NLP) to extract themes, sentiment, and common keywords.
   • title: The title given to the review by the user, often summarizing their main point.
   • isEdited: A boolean flag indicating whether the review has been edited by the user since its initial submission. This can be important for tracking evolving sentiment or understanding user behavior.

2. Reviewer & Rating Information:

   • username: The public username of the reviewer, allowing for analysis of engagement patterns from specific users (though not personally identifiable).
   • rating: The star rating (typically 1-5) given by the user, providing a quantifiable measure of satisfaction.

3. App & Origin Context:

   • app_name: The name of the application being reviewed.
   • app_id: A unique identifier for the application within the App Store, enabling direct linking to app details or other datasets.
   • country: The country of the App Store storefront where the review was left, allowing for geographic segmentation of feedback.

4. Metadata & Timestamps:

   • _id: A unique identifier for the specific review record in the dataset.
   • crawled_at: The timestamp indicating when this particular review record was collected by the data provider (Crawl Feeds).
   • date: The original date the review was posted by the user on the App Store.

Expanded Use Cases & Analytical Applications:

This dataset is a goldmine for understanding what users truly think and feel about mobile applications. Here's how it can be leveraged:

• Product Development & Improvement:

  • Bug Detection & Prioritization: Analyze negative review text to identify recurring technical issues, crashes, or bugs, allowing developers to prioritize fixes based on user impact.
  • Feature Requests & Roadmap Prioritization: Extract feature suggestions from positive and neutral review text to inform future product roadmap decisions and develop features users actively desire.
  • User Experience (UX) Enhancement: Understand pain points related to app design, navigation, and overall usability by analyzing common complaints in the review field.
  • Version Impact Analysis: If integrated with app version data, track changes in rating and sentiment after new app updates to assess the effectiveness of bug fixes or new features.

• Market Research & Competitive Intelligence:

  • Competitor Benchmarking: Analyze reviews of competitor apps (if included or combined with similar datasets) to identify their strengths, weaknesses, and user expectations within a specific app category.
  • Market Gap Identification: Discover unmet user needs or features that users desire but are not adequately provided by existing apps.
  • Niche Opportunities: Identify specific use cases or user segments that are underserved based on recurring feedback.

• Marketing & App Store Optimization (ASO):

  • Sentiment Analysis: Perform sentiment analysis on the review and title fields to gauge overall user satisfaction, pinpoint specific positive and negative aspects, and track sentiment shifts over time.
  • Keyword Optimization: Identify frequently used keywords and phrases in reviews to optimize app store listings, improving discoverability and search ranking.
  • Messaging Refinement: Understand how users describe and use the app in their own words, which can inform marketing copy and advertising campaigns.
  • Reputation Management: Monitor rating trends and identify critical reviews quickly to facilitate timely responses and proactive customer engagement.

• Academic & Data Science Research:

  • Natural Language Processing (NLP): The review and title fields are excellent for training and testing NLP models for sentiment analysis, topic modeling, named entity recognition, and text summarization.
  • User Behavior Analysis: Study patterns in rating distribution, isEdited status, and date to understand user engagement and feedback cycles.
  • Cross-Country Comparisons: Analyze country-specific reviews to understand regional differences in app perception, feature preferences, or cultural nuances in feedback.

This App Store Reviews dataset provides a direct, unfiltered conduit to understanding user needs and ultimately driving better app performance and greater user satisfaction. Its structured format and granular detail make it an indispensable asset for data-driven decision-making in the mobile app industry.

This dataset contains 54,987 UI screenshots and the metadata from 7,748 Android applications belonging to 25 application categories

As COVID-19 continues to spread across the world, a growing number of malicious campaigns are exploiting the pandemic. It is reported that COVID-19 is being used in a variety of online malicious activities, including Email scam, ransomware and malicious domains. As the number of the afflicted cases continue to surge, malicious campaigns that use coronavirus as a lure are increasing. Malicious developers take advantage of this opportunity to lure mobile users to download and install malicious apps.

However, besides a few media reports, the coronavirus-themed mobile malware has not been well studied. Our community lacks of the comprehensive understanding of the landscape of the coronavirus-themed mobile malware, and no accessible dataset could be used by our researchers to boost COVID-19 related cybersecurity studies.

We make efforts to create a daily growing COVID-19 related mobile app dataset. By the time of mid-November, we have curated a dataset of 4,322 COVID-19 themed apps, and 611 of them are considered to be malicious. The number is growing daily and our dataset will update weekly. For more details, please visit https://covid19apps.github.io

This dataset includes the following files:

(1) covid19apps.xlsx

In this file, we list all the COVID-19 themed apps information, including apk file hashes, released date, package name, AV-Rank, etc.

(2)covid19apps.zip

We put the COVID-19 themed apps Apk samples in zip files . In order to reduce the size of a single file, we divide the sample into multiple zip files for storage. And the APK file name after the file SHA256.

If your papers or articles use our dataset, please use the following bibtex reference to cite our paper: https://arxiv.org/abs/2005.14619

(Accepted to Empirical Software Engineering)

@misc{wang2021virus, title={Beyond the Virus: A First Look at Coronavirus-themed Mobile Malware}, author={Liu Wang and Ren He and Haoyu Wang and Pengcheng Xia and Yuanchun Li and Lei Wu and Yajin Zhou and Xiapu Luo and Yulei Sui and Yao Guo and Guoai Xu}, year={2021}, eprint={2005.14619}, archivePrefix={arXiv}, primaryClass={cs.CR} }

As of May 2023, Facebook collected the larger number of total unique data points from global iOS users, around 32 data points. Popular digital payment app PayPal and Airbnb collected 26 data points each, while AI tool photo and image editing apps Photoleap collected around 14 unique data points.

We surveyed 10,208 people from more than 15 countries on their mobile app usage behavior. The countries include USA, China, Japan, Germany, France, Brazil, UK, Italy, Russia, India, Canada, Spain, Australia, Mexico, and South Korea. We asked respondents about: (1) their mobile app user behavior in terms of mobile app usage, including the app stores they use, what triggers them to look for apps, why they download apps, why they abandon apps, and the types of apps they download. (2) their demographics including gender, age, marital status, nationality, country of residence, first language, ethnicity, education level, occupation, and household income (3) their personality using the Big-Five personality traits This dataset contains the results of the survey.

Google Play Store dataset to explore detailed information about apps, including ratings, descriptions, updates, and developer details. Popular use cases include app performance analysis, market research, and consumer behavior insights.

Use our Google Play Store dataset to explore detailed information about apps available on the platform, including app titles, developers, monetization features, user ratings, reviews, and more. This dataset also includes data on app descriptions, safety measures, download counts, recent updates, and compatibility, providing a complete overview of app performance and features.

Tailored for app developers, marketers, and researchers, this dataset offers valuable insights into user preferences, app trends, and market dynamics. Whether you're optimizing app development, conducting competitive analysis, or tracking app performance, the Google Play Store dataset is an essential resource for making data-driven decisions in the mobile app ecosystem.

Dataset Features

• url: The URL link to the app’s detail page on the Google Play Store.
• title: The name of the application.
• developer: The developer or company behind the app.
• monetization_features: Information regarding how the app generates revenue (e.g., in-app purchases, ads).
• images: Links or references to images associated with the app.
• about: Details or a summary description of the app.
• data_safety: Information regarding data safety and privacy practices.
• rating: The overall rating of the app provided by its users.
• number_of_reviews: The total count of user reviews received.
• star_reviews: A breakdown of reviews by star ratings.
• reviews: Reviews and user feedback about the app.
• what_new: Information on the latest updates or features added to the app.
• more_by_this_developer: Other apps by the same developer.
• content_rating: The content rating which guides suitability based on user age.
• downloads: The download count or range indicating the app’s popularity.
• country: The country associated with the app listing.
• app_category: The category or genre under which the app is classified.

Distribution

• Data Volume: 17 Columns and 65.54M Rows
• Format: CSV

Usage

This dataset is ideal for a variety of applications:

• App Market Analysis: Enables market researchers to extract insights on app popularity, engagement, and trends across different categories.
• Machine Learning: Can be used by data scientists to build recommendation engines or sentiment analysis models based on app review data.
• User Behavior Studies: Facilitates academic or industrial research into user preferences and behavior with respect to mobile applications.

Coverage

• Geographic Coverage: global.

License

CUSTOM Please review the respective licenses below: 1. Data Provider's License - Bright Data Master Service Agreement

Who Can Use It

• Data Scientists: To train machine learning models for app popularity prediction, sentiment analysis, or recommendation systems.
• Researchers: For academic or scientific studies into market trends, consumer behavior, and app performance analysis.
• Businesses: For strategic analysis, developing market insights, or enhancing app development and user engagement strategies.

Suggested Dataset Name

1. Play store Insights
2. Android App Scope
3. Market Analytics
4. Play Store Metrics Vault

5. AppTrend360: Google Play Edition

Pricing

Based on Delivery frequency

~Up to $0.0025 per record. Min order $250

Approximately 10M new records are added each month. Approximately 13.8M records are updated each month. Get the complete dataset each delivery, including all records. Retrieve only the data you need with the flexibility to set Smart Updates.

• Monthly

New snapshot each month, 12 snapshots/year Paid monthly

• Quarterly

New snapshot each quarter, 4 snapshots/year Paid quarterly

• Bi-annual

New snapshot every 6 months, 2 snapshots/year Paid twice-a-year

• One-time purchase

New snapshot one-time delivery Paid once

Nowadays, mobile applications (a.k.a., apps) are used by over two billion users for every type of need, including social and emergency connectivity. Their pervasiveness in today world has inspired the software testing research community in devising approaches to allow developers to better test their apps and improve the quality of the tests being developed. In spite of this research effort, we still notice a lack of empirical analyses aiming at assessing the actual quality of test cases manually developed by mobile developers: this perspective could provide evidence-based findings on the future research directions in the field as well as on the current status of testing in the wild. As such, we performed a large-scale empirical study targeting 1,780 open-source Android apps and aiming at assessing (1) the extent to which these apps are actually tested, (2) how well-designed are the available tests, and (3) what is their effectiveness. The key results of our study show that mobile developers still tend not to properly test their apps, possibly because of time to market requirements. Furthermore, we discovered that the test cases of the considered apps have a low (i) design quality, both in terms of test code metrics and test smells, and (ii) effectiveness when considering code coverage as well as assertion density.

This dataset encompasses a wide-ranging collection of Google Play applications, providing a holistic view of the diverse ecosystem within the platform. It includes information on various attributes such as the title, developer, monetization features, images, app descriptions, data safety measures, user ratings, number of reviews, star rating distributions, user feedback, recent updates, related applications by the same developer, content ratings, estimated downloads, and timestamps. By aggregating this data, the dataset offers researchers, developers, and analysts an extensive resource to explore and analyze trends, patterns, and dynamics within the Google Play Store. Researchers can utilize this dataset to conduct comprehensive studies on user behavior, market trends, and the impact of various factors on app success. Developers can leverage the insights derived from this dataset to inform their app development strategies, improve user engagement, and optimize monetization techniques. Analysts can employ the dataset to identify emerging trends, assess the performance of different categories of applications, and gain valuable insights into consumer preferences. Overall, this dataset serves as a valuable tool for understanding the broader landscape of the Google Play Store and unlocking actionable insights for various stakeholders in the mobile app industry.

This dataset is about books. It has 1 row and is filtered where the book is Build mobile apps with Ionic 2 and Firebase : hybrid mobile app development. It features 7 columns including author, publication date, language, and book publisher.

This dataset comprises 10,000 user reviews of the BCA Mobile app collected from the Google Play Store between December 24, 2023, and June 12, 2024. Each review includes the user's name, the rating they provided (ranging from 1 to 5 stars), the timestamp of when the review was created, and the text content of the review. The dataset is in Indonesian and focuses on feedback from users in Indonesia. This data can be used to perform sentiment analysis, understand user experiences, identify common issues, and assess the overall performance of the BCA Mobile app during the specified timeframe. The reviews are sorted based on the newest first, providing the latest feedback at the top.

the device was left without user interaction for 5 minutes.

App Download Key StatisticsApp and Game DownloadsiOS App and Game DownloadsGoogle Play App and Game DownloadsGame DownloadsiOS Game DownloadsGoogle Play Game DownloadsApp DownloadsiOS App...

AndroR2 is a dataset of 90 manually reproduced bug reports for Android apps listed on Google Play and hosted on GitHub, systematically collected via an in-depth analysis of 459 reports extracted from the GitHub issue tracker. For each reproduced report, AndroR2 includes the original bug report, an apk file for the buggy version of the app, an executable reproduction script, and metadata regarding the quality of the reproduction steps associated with the original report. We believe that the AndroR2 dataset can be used to facilitate research in automatically analyzing, understanding, reproducing, localizing, and fixing bugs for mobile applications as well as other software maintenance activities more broadly.

As of January 2025, around ** percent of the data linked to users collected by iOS apps was used by app publishers to integrate their product's functionalities. In comparison, ** percent of app data not directly linked to users had the same function. Collecting analytics data was the second most common reason for apps to collect iOS users' data, while only ** percent of identifiable user data and ** percent of non-identifiable users' data went to improve or integrate third-party advertising services.

This dataset includes network traffic data from more than 50 Android applications across 5 different scenarios. The applications are consistent in all scenarios, but other factors like location, device, and user vary (see Table 2 in the paper). The current repository pertains to Scenario C. Within the repository, for each application, there is a compressed file containing the relevant PCAP files. The PCAP files follow the naming convention: {Application Name}{Scenario ID}{#Trace}_Final.pcap.