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

Do you know?

Do you know how much time you spend on an app? Do you know the total use time of a day or average use time of an app?

What it consists of?

This data set consists of - how many times a person unlocks his phone. - how much time he spends on every app on every day. - how much time he spends on his phone.

It lists the usage time of apps for each day.

What we can do?

Use the test data to find the Total Minutes that we can use the given app in a day. we can get a clear stats of apps usage. This data set will show you about the persons sleeping behavior as well as what app he spends most of his time. with this we can improve the productivity of the person.

The dataset was collected from the app usage app.

Key US App Market StatisticsUS App Market SizeUS App Market Revenue by AppUS Smartphone UsersUS Smartphone PopulationTime Spent on Apps in the USUS App Market DownloadsUS Downloads by AppUS Daily...

How many people use social media?

              Social media usage is one of the most popular online activities. In 2024, over five billion people were using social media worldwide, a number projected to increase to over six billion in 2028.

              Who uses social media?
              Social networking is one of the most popular digital activities worldwide and it is no surprise that social networking penetration across all regions is constantly increasing. As of January 2023, the global social media usage rate stood at 59 percent. This figure is anticipated to grow as lesser developed digital markets catch up with other regions
              when it comes to infrastructure development and the availability of cheap mobile devices. In fact, most of social media’s global growth is driven by the increasing usage of mobile devices. Mobile-first market Eastern Asia topped the global ranking of mobile social networking penetration, followed by established digital powerhouses such as the Americas and Northern Europe.

              How much time do people spend on social media?
              Social media is an integral part of daily internet usage. On average, internet users spend 151 minutes per day on social media and messaging apps, an increase of 40 minutes since 2015. On average, internet users in Latin America had the highest average time spent per day on social media.

              What are the most popular social media platforms?
              Market leader Facebook was the first social network to surpass one billion registered accounts and currently boasts approximately 2.9 billion monthly active users, making it the most popular social network worldwide. In June 2023, the top social media apps in the Apple App Store included mobile messaging apps WhatsApp and Telegram Messenger, as well as the ever-popular app version of Facebook.

Apple App Store Key StatisticsApps & Games in the Apple App StoreApps in the Apple App StoreGames in the Apple App StoreMost Popular Apple App Store CategoriesPaid vs Free Apps in Apple App...

[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!

During the first quarter of 2024, YouTube shorts recorded the highest engagement rate across all short video platforms and in-app features analyzed. Content hosted on YouTube in form of shorts had an engagement rate of 5.91 percent, while TikTok reported an engagement rate of approximately 5.75 percent. Facebook Reels had an engagement rate of around two percent, making the platform rank last for short-format user engagement.

As of May 2023, approximately 98 percent of all data collected by travel and mobility iOS apps were linked to the users' identity. However, only 17 percent of the collected data were users to track users of apps in this category. Shopping and food delivery apps used 36.4 percent of the collected data for tracking purposes, while AI tool apps hosted on the Apple App Store used 35.6 percent of the collected data for tracking their users.

The pendulum swung in 2022 with app downloads stagnating, after two years of solid growth under the pandemic. In 2023, some categories saw growth while others continued to stagnate, as users shifted...

Between February 2023 and 2024, female mobile gamers worldwide spent an average of 21.6 minutes daily on word games, compared to only 20.9 minutes among male mobile gaming audiences. Male gamers in Latin America had the lowest daily user engagement with this genre.

📌 Introduction

This dataset is a synthetic yet realistic simulation of newborn baby health monitoring.
It is designed for healthcare analytics, machine learning, and app development, especially for early detection of newborn health risks.

The dataset mimics daily health records of newborn babies, including vital signs, growth parameters, feeding patterns, and risk classification labels.

🎯 Motivation

Newborn health is one of the most sensitive areas of healthcare.
Monitoring newborns can help detect jaundice, infections, dehydration, and respiratory issues early.

Since real newborn data is private and hard to access, this dataset provides a safe and realistic alternative for researchers, students, and developers to build and test:
- 📊 Exploratory Data Analysis (EDA)
- 🤖 Machine Learning classification models
- 📱 Healthcare monitoring apps (Streamlit, Flask, Django, etc.)
- 🏥 Predictive healthcare systems

📂 Dataset Overview

• Total Babies: 100
• Monitoring Period: 30 days per baby
• Total Records: 3,000
• File Format: CSV
• Synthetic Data: Generated using Python (pandas, numpy, faker) with medically-informed rules

📑 Column Description

🔹 Demographics

• baby_id → Unique identifier for each baby (e.g., B001).
• name → Randomly generated baby first name (for realism).
• gender → Male / Female.
• gestational_age_weeks → Gestational age at birth (normal: 37–42 weeks).
• birth_weight_kg → Birth weight (normal range: 2.5–4.5 kg).
• birth_length_cm → Length at birth (avg: 48–52 cm).
• birth_head_circumference_cm → Head circumference at birth (avg: 33–35 cm).

🔹 Daily Monitoring

• date → Monitoring date.
• age_days → Age of baby in days since birth.
• weight_kg → Daily updated weight (growth trend ~25–30g/day).
• length_cm → Daily updated body length (slow increase).
• head_circumference_cm → Daily updated head circumference.
• temperature_c → Body temperature in °C (normal: 36.5–37.5°C).
• heart_rate_bpm → Heart rate (normal: 120–160 bpm).
• respiratory_rate_bpm → Breathing rate (normal: 30–60 breaths/min).
• oxygen_saturation → SpO₂ level (normal >95%).

🔹 Feeding & Hydration

• feeding_type → Breastfeeding / Formula / Mixed.
• feeding_frequency_per_day → Number of feeds per day (normal: 8–12).
• urine_output_count → Wet diapers/day (normal: 6–8+).
• stool_count → Bowel movements per day (0–5 is common).

🔹 Medical Screening

• jaundice_level_mg_dl → Bilirubin level (normal <5, mild 5–12, severe >15).
• apgar_score → 0–10 score at birth (only day 1).
• immunizations_done → Yes/No (BCG, HepB, OPV on Day 1 & 30).
• reflexes_normal → Newborn reflex check (Yes/No).

🔹 Risk Classification

• risk_level → Automatically assigned health status:
  • ✅ Healthy → All vitals normal.
  • ⚠️ At Risk → Mild abnormalities (e.g., mild jaundice, slight fever, SpO₂ 92–95%).
  • 🚨 Critical → Severe abnormalities (e.g., jaundice >15, SpO₂ <92, HR >180, temp >39°C).

📊 How Data Was Generated

The dataset was generated in Python using:
- numpy and pandas for data simulation.
- faker for generating baby names and dates.
- Medically realistic rules for vitals, growth, jaundice progression, and risk classification.

💡 Potential Applications

• Machine Learning: Train classification models to predict newborn health risks.
• Streamlit/Dash Apps: Build real-time newborn monitoring dashboards.
• Healthcare Research: Study growth and vital sign patterns.
• Education: Practice EDA, visualization, and predictive modeling on health datasets.

📬 Author & Contact

Created by [Arif Miah]
I am passionate about AI, Healthcare Analytics, and App Development.
You can connect with me:

• 📺 YouTube → [https://www.youtube.com/@intelliaiworld]
• 💼 LinkedIn → [www.linkedin.com/in/arif-miah-8751bb217]
• 💻 GitHub → [https://github.com/Arif-miad]

⚠️ Disclaimer

This is a synthetic dataset created for educational and research purposes only.
It should NOT be used for actual medical diagnosis or treatment decisions.

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} }

Mobility/Location data is gathered from location-aware mobile apps using an SDK-based implementation. All users explicitly consent to allow location data sharing using a clear opt-in process for our use cases and are given clear opt-out options. Factori ingests, cleans, validates, and exports all location data signals to ensure only the highest quality of data is made available for analysis.

Record Count:90 Billion+ Capturing Frequency: Once per Event Delivering Frequency: Once per Day Updated: Daily

Mobility Data Reach: Our data reach represents the total number of counts available within various categories and comprises attributes such as country location, MAU, DAU & Monthly Location Pings.

Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited interval (daily/weekly/monthly/quarterly).

Here's the major usecases being served: Consumer Insight: Gain a comprehensive 360-degree perspective of the customer to spot behavioral changes, analyze trends and predict business outcomes. Market Intelligence: Study various market areas, the proximity of points or interests, and the competitive landscape. Advertising: Create campaigns and customize your messaging depending on your target audience's online and offline activity. Retail Analytics: Analyze footfall trends in various locations and gain an understanding of customer personas.

Schema - maid latitude longitude horizontal_accuracy timestamp id_type ipv4 ipv6 user_agent country state_hasc city_hasc postcode geohash hex8 hex9 carrier

This data includes the name and location of active food service establishments and the violations that were found at the time of the inspection. Active food service establishments include only establishments that are currently operating. This dataset excludes inspections conducted in New York City (https://data.cityofnewyork.us/Health/Restaurant-Inspection-Results/4vkw-7nck), Suffolk County (http://apps.suffolkcountyny.gov/health/Restaurant/intro.html) and Erie County (http://www.healthspace.com/erieny). Inspections are a “snapshot” in time and are not always reflective of the day-to-day operations and overall condition of an establishment. Occasionally, remediation may not appear until the following month due to the timing of the updates. Update frequencies and availability of historical inspection data may vary from county to county. Some counties provide this information on their own websites and information found there may be updated more frequently. This dataset is refreshed on a monthly basis. The inspection data contained in this dataset was not collected in a manner intended for use as a restaurant grading system, and should not be construed or interpreted as such. Any use of this data to develop a restaurant grading system is not supported or endorsed by the New York State Department of Health. For more information, visit http://www.health.ny.gov/regulations/nycrr/title_10/part_14/subpart_14-1.htm or go to the “About” tab.

This dataset contains daily reservoir storage data and statistics for a selected set of Reclamation reservoirs and reservoir systems. Reservoirs were chosen to include a selection of operationally significant reservoirs or reservoir systems in each Reclamation Region. Reservoir storage values for each included reservoir are updated daily from the water operations database of the Reclamation office that manages the reservoir.This dataset was created for use in the Reservoir Storage Dashboard, located at https://usbr.maps.arcgis.com/apps/dashboards/81aaec3e74024ce6b9a5e50caa20984e.More information about the dashboard and data can be found at https://data.usbr.gov/visualizations/reservoir-conditions/RISE Catalog Item 78633: https://data.usbr.gov/catalog/7964/item/78633To download data, please use the RISE Geospatial Open Data site: https://rise-usbr.opendata.arcgis.com/datasets/3ee1b2d5ebc0435583bdb5e30e51f01b

How much time do people spend on social media? As of 2025, the average daily social media usage of internet users worldwide amounted to 141 minutes per day, down from 143 minutes in the previous year. Currently, the country with the most time spent on social media per day is Brazil, with online users spending an average of 3 hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in the U.S. was just 2 hours and 16 minutes. Global social media usageCurrently, the global social network penetration rate is 62.3 percent. Northern Europe had an 81.7 percent social media penetration rate, topping the ranking of global social media usage by region. Eastern and Middle Africa closed the ranking with 10.1 and 9.6 percent usage reach, respectively. People access social media for a variety of reasons. Users like to find funny or entertaining content and enjoy sharing photos and videos with friends, but mainly use social media to stay in touch with current events friends. Global impact of social mediaSocial media has a wide-reaching and significant impact on not only online activities but also offline behavior and life in general. During a global online user survey in February 2019, a significant share of respondents stated that social media had increased their access to information, ease of communication, and freedom of expression. On the flip side, respondents also felt that social media had worsened their personal privacy, increased a polarization in politics and heightened everyday distractions.

ChatGPT was the chatbot that kickstarted the generative AI revolution, which has been responsible for hundreds of billions of dollars in data centres, graphics chips and AI startups. Launched by...

As of May 2023, the mobile app version of popular first-person shooter Call of Duty used 10 of the data points they collected to track their iOS users, as well as collecting 17 data points connected to the user's identity. Facebook, which was identified as the most data-hungry app among all the mobile social media, used seven of its 32 collected data points to track users. Dating app Bumble collected 22 data points collected to the users' identity, as well as four data points to track users activity.

Dataset Structure

Columns: 1. User ID: Unique identifier for each user 2. Date: Date of the activity 3. Step Count: Number of steps taken 4. Distance (km): Distance covered in kilometers 5. Calories Burned: Total calories burned 6. Active Minutes: Total minutes of physical activity 7. Workout Type: Type of workout (e.g., Running, Walking, Cycling, Swimming) 8. Duration (min): Duration of the workout in minutes 9. Heart Rate (bpm): Average heart rate during the activity 10. Sleep Duration (hours): Total hours of sleep 11. Sleep Quality: Quality of sleep (e.g., Good, Fair, Poor) 12. Water Intake (liters): Amount of water consumed 13. Calories Intake: Total calories consumed 14. Weight (kg): Weight of the user 15. Mood: Self-reported mood (e.g., Happy, Stressed, Tired) 16. Notes: Any additional notes about the day or workout

Example Entry:

Data Collection Methods: 1. Wearable Devices: Smartwatches or fitness trackers can provide step count, distance, calories burned, heart rate, and active minutes. 2. Mobile Apps: Health apps can log workout types, durations, and track water and calorie intake. 3. Manual Entry: Users can manually enter sleep quality, mood, weight, and notes. 4. Integrations: Integrate with other health apps and devices for comprehensive data collection.

Usage: - Personal Fitness Tracking: Individuals can monitor their progress and adjust their routines. - Research: Anonymized datasets can be used for studies on physical activity and health outcomes. - Health Monitoring: Healthcare providers can use the data for monitoring patient health and recommending interventions.

Mobility/Location data is gathered from location-aware mobile apps using an SDK-based implementation. All users explicitly consent to allow location data sharing using a clear opt-in process for our use cases and are given clear opt-out options. Factori ingests, cleans, validates, and exports all location data signals to ensure only the highest quality of data is made available for analysis.

Record Count:90 Billion+ Capturing Frequency: Once per Event Delivering Frequency: Once per Day Updated: Daily

Mobility Data Reach: Our data reach represents the total number of counts available within various categories and comprises attributes such as country location, MAU, DAU & Monthly Location Pings.

Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited interval (daily/weekly/monthly/quarterly).

Business Needs: Consumer Insight: Gain a comprehensive 360-degree perspective of the customer to spot behavioral changes, analyze trends and predict business outcomes. Market Intelligence: Study various market areas, the proximity of points or interests, and the competitive landscape. Advertising: Create campaigns and customize your messaging depending on your target audience's online and offline activity. Retail Analytics Analyze footfall trends in various locations and gain understanding of customer personas.

Here's the data attributes: maid latitude longtitude horizontal_accuracy timestamp id_type ipv4 ipv6 user_agent country state_hasc city_hasc hex8 hex9 carrier