Android maintained its position as the leading mobile operating system worldwide in the first 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.

CSV file with code smell occurrences per application. One file for iOS and one for Android. Analysis of open source applications.

The number of smartphone users in the United States was forecast to continuously increase between 2024 and 2029 by in total 17.4 million users (+5.61 percent). After the fifteenth consecutive increasing year, the smartphone user base is estimated to reach 327.54 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 Mexico and Canada.

This is a public release of Beiwe-generated data. The Beiwe Research Platform collects high-density data from a variety of smartphone sensors such as GPS, WiFi, Bluetooth, gyroscope, and accelerometer in addition to metadata from active surveys. A description of passive and active data streams, and a documentation concerning the use of Beiwe can be found here. This data was collected from an internal test study and is made available solely for educational purposes. It contains no identifying information; subject locations are de-identified using the noise GPS feature of Beiwe.

As part of the internal test study, data from 6 participants were collected from the start of March 21, 2022 to the end of March 28, 2022. The local time zone of this study is Eastern Standard Time. Each participant was notified to complete a survey at 9am EST on Monday, Thursday, and Saturday of the study week. An additional survey was administered on Tuesday at 5:15pm EST. For each survey, subjects were asked to respond to the prompt "How much time (in hours) do you think you spent at home?".

Context Imagine you have to process bug reports about an application your company is developing, which is available for both Android and iOS. Could you find a way to automatically classify them so you can send them to the right support team?

Content The dataset contains data from two StackExchange forums: Android Enthusiasts and Ask Differently (Apple). I pre-processed both datasets from the raw XML files retrieved from Internet Archive in order to only contain useful information for building Machine Learning classifiers. In the case of the Apple forum, I narrowed down to the subset of questions that have one of the following tags: "iOS", "iPhone", "iPad".

Think of this as a fun way to learn to build ML classifiers! The training, validation and test sets are all available, but in order to build robust models please try to use the test set as little as possible (only as a last validation for your models).

Acknowledgements The image was retrieved from unsplash and made by @thenewmalcolm. Link to image here.

The data was made available for free under a CC-BY-SA 4.0 license by StackExchange and hosted by Internet Archive. Find it here.

License

CC-BY-SA

Original Data Source: Question Classification: Android or iOS?

Casual smartphone 3D scans using three devices: iPhone 15 Pro, Samsung Galaxy S20 FE and Google Pixel 5. The raw data (prefixed "spectacular-rec-") has been captured with the Spectacular Rec applications for Android and iOS, and contains time-synchronized video and IMU data. The extras file also contains AprilGrid calibration sequences, as well as pre-computed calibration results, for the Android devices.

The data is captured in non-ideal lighting conditions and has a moderate amount of motion blur and rolling shutter artefacts. The included metadata also contains the exposure times and the (Android) rolling shutter readout times, as well as the built-in calibration data, as reported by the devices.

The dataset also contains three different processed variants (prefixed with "colmap-"), which are directly trainable with Nerfstudio. In the processed variants, suitable minimally blurry video frames have been selected as key frames and their poses have been registered with COLMAP. In addition, the local linear and angular velocities of each key frame has been estimated using VIO with Spectacular AI Mapping Tools. The "calib-intrinsics" and "orig-intrinsics" variants include manually calibrated and built-in intrinsics, respectively. They depend on the third variant with symbolic links. The third variant has COLMAP-estimated intrinsics, which are relatively inaccurate for the Android data with high levels rolling shutter deformation.

Differences between operating systems (Android, iOS, Mac OS, and Windows; Study 2).

Context

App Permission data of 2.2 million android applications from Google Play store. Backup repo: https://github.com/gauthamp10/android-permissions-dataset

Content

I've collected the data with the help of Python and Scrapy running on a cloud virtual machine with the United States as geolocation. The data was collected on June 2021.

Also checkout:

• Google Play Store Apps dataset: https://www.kaggle.com/gauthamp10/google-playstore-apps
• Apple AppStore Apps dataset: https://www.kaggle.com/gauthamp10/apple-appstore-apps

Acknowledgements

I couldn't have build this dateset without the help of Digitalocean and github. Switched to facundoolano/google-play-scraper for sane reasons.

Inspiration

Took inspiration from: https://www.kaggle.com/gauthamp10/google-playstore-apps to build a big database for students and researchers who are interested to analyze and find insights on mobile application privacy.

Author

Gautham Prakash

My other projects: github.com/gauthamp10

Website: gauthamp10.github.io

Global Cloud Mobile Backend as a Service (BaaS) Market size was $3.0 Billion in 2022 and is slated to hit $7.3 Billion by the end of 2030 with a CAGR of nearly 24.1%.

Loudoun Express Request (LEx) is a citizen request system for members of the public to submit requests for service and report concerns to the county government via the internet and a mobile application. Our goal is to increase the efficiency, security, and accountability in responding to citizen concerns and questions. LEx is available as a mobile app for iOS and Android users!

This is our initial public release of Beiwe-generated data. The Beiwe Research Platform collects high-density data from a variety of smartphone sensors including GPS, WiFi, Bluetooth, and accelerometer. To learn more about Beiwe, check out the Onnela Lab page, the paper introducing the platform, or the Beiwe wiki.

Examples of how to use the data and more information will be updated on the Github repo: https://github.com/mkiang/beiwe_data_sample

Report issues via the Github repo.

This dataset encompasses 2.5 billion annual data points on location visits, app usage, and mobile web clickstream activities. Collected from over 100,000 triple-opt-in first-party U.S. Daily Active Users (DAU), it offers a robust foundation for understanding consumer behaviors.

At its core, this dataset contains unstructured event-level data, capturing both brick-and-mortar and app + web visits and interactions. The data is collected from both iOS and Android smartphones, providing an in-depth analysis and interpretation of validated consumer behaviors.

One of the key strengths of this dataset, is its utilization of OmniTraffic technology, which seamlessly integrates location, app, and web behaviors from individual consumers. By meticulously tracking the "who, what, where and when" of both online and offline visits, it provides comprehensive insights into consumer journeys.

Moreover, this dataset goes beyond mere observation by incorporating validated behaviors to uncover the underlying motivations driving consumer decisions. This deeper understanding of "the why" behind behaviors sets it apart, offering invaluable insights into consumer preferences and trends.

• 4+ year history
• iOS & Android

The primary use cases and verticals of our Behavioral Data Product are diverse and varied. Some key applications include:

• Data Acquisition and Modeling: Our data helps businesses acquire valuable insights into consumer behavior and enables modeling for various research objectives.

• Shopper Data Analysis: By understanding purchase behavior and patterns, businesses can optimize their strategies, improve targeting, and enhance customer experiences.

• Media Consumption Insights: Our data provides a deep understanding of viewer behavior and patterns across popular platforms like YouTube, Amazon Prime, Netflix, and Disney+, enabling effective media planning and content optimization.

• App Performance Optimization: Analyzing app behavior allows businesses to monitor usage patterns, track key performance indicators (KPIs), and optimize app experiences to drive user engagement and retention.

• Location-Based Targeting: With our detailed location data, businesses can map out consumer visits to physical venues and combine them with web and app behavior to create predictive ad targeting strategies.

• Audience Creation for Ad Placement: Our data enables the creation of highly targeted audiences for ad campaigns, ensuring better reach and engagement with relevant consumer segments.

The Behavioral Data Product complements our comprehensive suite of data solutions in the broader context of our data offering. It provides granular and event-level insights into consumer behaviors, which can be combined with other data sets such as survey responses, demographics, or custom profiling questions to offer a holistic understanding of consumer preferences, motivations, and actions.

MFour's Behavioral Data empowers businesses with unparalleled consumer insights, allowing them to make data-driven decisions, uncover new opportunities, and stay ahead in today's dynamic market landscape.

This data release includes two Wikipedia datasets related to the readership of the project as it relates to the early COVID-19 pandemic period. The first dataset is COVID-19 article page views by country, the second dataset is one hop navigation where one of the two pages are COVID-19 related. The data covers roughly the first six months of the pandemic, more specifically from January 1st 2020 to June 30th 2020. For more background on the pandemic in those months, see English Wikipedia's Timeline of the COVID-19 pandemic.Wikipedia articles are considered COVID-19 related according the methodology described here, the list of COVID-19 articles used for the released datasets is available in covid_articles.tsv. For simplicity and transparency, the same list of articles from 20 April 2020 was used for the entire dataset though in practice new COVID-19-relevant articles were constantly being created as the pandemic evolved.Privacy considerationsWhile this data is considered valuable for the insight that it can provide about information-seeking behaviors around the pandemic in its early months across diverse geographies, care must be taken to not inadvertently reveal information about the behavior of individual Wikipedia readers. We put in place a number of filters to release as much data as we can while minimizing the risk to readers.The Wikimedia foundation started to release most viewed articles by country from Jan 2021. At the beginning of the COVID-19 an exemption was made to store reader data about the pandemic with additional privacy protections:- exclude the page views from users engaged in an edit session- exclude reader data from specific countries (with a few exceptions)- the aggregated statistics are based on 50% of reader sessions that involve a pageview to a COVID-19-related article (see covid_pages.tsv). As a control, a 1% random sample of reader sessions that have no pageviews to COVID-19-related articles was kept. In aggregate, we make sure this 1% non-COVID-19 sample and 50% COVID-19 sample represents less than 10% of pageviews for a country for that day. The randomization and filters occurs on a daily cadence with all timestamps in UTC.- exclude power users - i.e. userhashes with greater than 500 pageviews in a day. This doubles as another form of likely bot removal, protects very heavy users of the project, and also in theory would help reduce the chance of a single user heavily skewing the data.- exclude readership from users of the iOS and Android Wikipedia apps. In effect, the view counts in this dataset represent comparable trends rather than the total amount of traffic from a given country. For more background on readership data per country data, and the COVID-19 privacy protections in particular, see this phabricator.To further minimize privacy risks, a k-anonymity threshold of 100 was applied to the aggregated counts. For example, a page needs to be viewed at least 100 times in a given country and week in order to be included in the dataset. In addition, the view counts are floored to a multiple of 100.DatasetsThe datasets published in this release are derived from a reader session dataset generated by the code in this notebook with the filtering described above. The raw reader session data itself will not be publicly available due to privacy considerations. The datasets described below are similar to the pageviews and clickstream data that the Wikimedia foundation publishes already, with the addition of the country specific counts.COVID-19 pageviewsThe file covid_pageviews.tsv contains:- pageview counts for COVID-19 related pages, aggregated by week and country- k-anonymity threshold of 100- example: In the 13th week of 2020 (23 March - 29 March 2020), the page 'Pandémie_de_Covid-19_en_Italie' on French Wikipedia was visited 11700 times from readers in Belgium- as a control bucket, we include pageview counts to all pages aggregated by week and country. Due to privacy considerations during the collection of the data, the control bucket was sampled at ~1% of all view traffic. The view counts for the control title are thus proportional to the total number of pageviews to all pages.The file is ~8 MB and contains ~134000 data points across the 27 weeks, 108 countries, and 168 projects.Covid reader session bigramsThe file covid_session_bigrams.tsv contains:- number of occurrences of visits to pages A -> B, where either A or B is a COVID-19 related article. Note that the bigrams are tuples (from, to) of articles viewed in succession, the underlying mechanism can be clicking on a link in an article, but it may also have been a new search or reading both articles based on links from third source articles. In contrast, the clickstream data is based on referral information only- aggregated by month and country- k-anonymity threshold of 100- example: In March of 2020, there were a 1000 occurences of readers accessing the page es.wikipedia/SARS-CoV-2 followed by es.wikipedia/Orthocoronavirinae from ChileThe file is ~10 MB and contains ~90000 bigrams across the 6 months, 96 countries, and 56 projects.ContactPlease reach out to research-feedback@wikimedia.org for any questions.

Note: This dataset has been archived and is no longer being updated. COVID Alert CT is Connecticut's voluntary, anonymous, exposure-notification smartphone app. If downloaded, the app will alert users if they have come into close contact with somebody who tests positive for COVID-19. This dataset includes the cumulative and weekly activations for COVID Alert CT for iOS and Android smartphones. The _location of app users is not tracked--the app uses Bluetooth technology to detect when another person with the same app comes within 6 feet. The phones exchange a secure code with the each other to record that they were near. The number of codes issued and claimed is also included in this dataset. Data presented are based on a weekly reporting period (Sunday - Saturday). All data are preliminary and are subject to change. Additional information on COVID-19 Contact Tracing can be found here: https://portal.ct.gov/coronavirus/covidalertCT/homepage

Descriptives of study 1 variables separated for operating systems (Android, iOS).

What’s Included

Introducing the French Newspaper, Books, and Magazine Image Dataset - a diverse and comprehensive collection of images meticulously curated to propel the advancement of text recognition and optical character recognition (OCR) models designed specifically for the French language.

Containing a total of 5000 images, this French OCR dataset offers an equal distribution across newspapers, books, and magazines. Within, you'll find a diverse collection of content, including articles, advertisements, cover pages, headlines, call outs, and author sections from a variety of newspapers, books, and magazines. Images in this dataset showcases distinct fonts, writing formats, colors, designs, and layouts.

To ensure the diversity of the dataset and to build robust text recognition model we allow limited (less than five) unique images from a single resource. Stringent measures have been taken to exclude any personal identifiable information (PII), and in each image a minimum of 80% space is contain visible French text.

Images have been captured under varying lighting conditions – both day and night – along with different capture angles and backgrounds, further enhancing dataset diversity. The collection features images in portrait and landscape modes.

All these images were captured by native French people to ensure the text quality, avoid toxic content and PII text. We used latest iOS and android mobile devices above 5MP camera to click all these images to maintain the image quality. In this training dataset images are available in both JPEG and HEIC formats.

Along with the image data you will also receive detailed structured metadata in CSV format. For each image it includes metadata like device information, source type like newspaper, magazine or book image, and image type like portrait or landscape etc. Each image is properly renamed corresponding to the metadata.

The metadata serves as a valuable tool for understanding and characterizing the data, facilitating informed decision-making in the development of French text recognition models.

We're committed to expanding this dataset by continuously adding more images with the assistance of our native French crowd community.

If you require a custom dataset tailored to your guidelines or specific device distribution, feel free to contact us. We're equipped to curate specialized data to meet your unique needs.

Furthermore, we can annotate or label the images with bounding box or transcribe the text in the image to align with your specific requirements using our crowd community.

This Image dataset, created by FutureBeeAI, is now available for commercial use.

Leverage the power of this image dataset to elevate the training and performance of text recognition, text detection, and optical character recognition models within the realm of the French language. Your journey to enhanced language understanding and processing starts here.

This dataset contains a collection of user reviews and ratings for the Livin' by Mandiri mobile application. Livin' by Mandiri is a digital financial service platform developed by Bank Mandiri, one of Indonesia's largest banks, offering features like payments, money transfers, and financial management on mobile devices for both Android and iOS users. The data was collected by scraping reviews from the Google Play Store, providing insights into user feedback and app performance.

Columns

The dataset is provided in a CSV file and includes the following columns: * date: The date when the user review was submitted, in datetime format. * review: The textual content of the user's review. * rating: The user's rating, on a scale of 1 to 5. * thumbs_up: The total number of 'thumbs up' or likes given by other users to that particular review. * version: The version of the app when the user submitted the review.

Distribution

The dataset is structured as a CSV file. It contains approximately 155,192 records, representing reviews submitted between 30 September 2021 and 24 December 2022.

Review counts show significant peaks at certain times: * 12/29/2021 - 01/07/2022: 16,439 reviews * 02/21/2022 - 03/02/2022: 11,191 reviews * 05/22/2022 - 05/31/2022: 10,247 reviews * 07/06/2022 - 07/15/2022: 10,477 reviews * 07/15/2022 - 07/24/2022: 11,011 reviews

Rating Distribution: * 4.92 - 5.00 (5-star equivalent): 86,215 reviews * 1.00 - 1.08 (1-star equivalent): 39,183 reviews * 3.96 - 4.04 (4-star equivalent): 10,951 reviews * 3.00 - 3.08 (3-star equivalent): 9,464 reviews * 1.96 - 2.04 (2-star equivalent): 9,379 reviews

Thumbs Up Distribution: * 0.00 - 47.94: 154,810 reviews (majority of reviews received low 'thumbs up' counts) * Higher counts are present but significantly less frequent, with a maximum of 2,397 thumbs up for a single review.

App Version Distribution: * 1.0.2: 28% of reviews * [null]: 24% of reviews (indicating no version information available for these reviews) * Other: 48% of reviews across various versions.

Usage

This dataset is ideal for: * Exploratory Data Analysis (EDA) to understand trends in user feedback. * Sentiment Analysis to gauge overall user satisfaction and identify emotional tones in reviews. * App performance monitoring and identifying areas for improvement based on user comments and ratings. * Market research into digital banking service perception in Indonesia. * Academic research on financial technology adoption and mobile app user behaviour.

Coverage

• Geographic Scope: The reviews are primarily from users in Indonesia, given that Bank Mandiri is a major Indonesian bank.
• Time Range: The data spans from 30 September 2021 to 24 December 2022.
• Demographic Scope: The dataset reflects feedback from users of the Livin' by Mandiri mobile app on Android and iOS devices.

License

CC-BY-NC

Who Can Use It

This dataset is beneficial for: * Data Analysts and Scientists: For performing EDA, sentiment analysis, and building predictive models related to user satisfaction. * App Developers and Product Managers: To understand user pain points, identify popular features, and guide future app updates. * Researchers: Studying digital finance, user experience, and mobile app ecosystems in emerging markets like Indonesia. * Business Intelligence Professionals: To inform strategic decisions based on customer feedback and market sentiment.

Dataset Name Suggestions

• Livin' by Mandiri App Reviews
• Indonesian Mobile Banking User Feedback
• Bank Mandiri Digital App Ratings
• Fintech App Reviews Indonesia
• Livin' App User Sentiment Data

Attributes

Original Data Source: Livin' by Mandiri App Reviews

Context

This is the dataset that I used in my iOS and Android plant disease detection app, PlantifyDr. You can check out my full open-source project here: https://github.com/lavaman131/PlantifyDr

Content

The dataset contains over 125,000 jpg images of 10 different plant types: Apple, Bell pepper, Cherry, Citrus, Corn, Grape, Peach, Potato, Strawberry, and Tomato. The total number of plant diseases is 37. Augmentations have already been applied to the data, but feel free to add your own augmentations if you like.

Acknowledgements

Special thanks to: https://data.mendeley.com/datasets/tywbtsjrjv/1 https://www.kaggle.com/vipoooool/new-plant-diseases-dataset https://github.com/pratikkayal/PlantDoc-Dataset https://data.mendeley.com/datasets/3f83gxmv57/2

for the data.

Inspiration

The Food and Agriculture Organization of the United Nations (FAO) estimates that annually between 20 to 40 percent of global crop production is lost. Each year, plant diseases cost the global economy around $220 billion. I hoped to use deep learning to solve this problem and be able to better educate farmers and the public with the necessary knowledge to treat their plants.

Datasets, published along side planned research paper "Evaluation of TCP/IP-based OS fingerprinting methods using new datasets". Datasets contain OS annotated networks flow, exported via ipfixprobe.

Four datasets (subnet1, subnet2, subnet3, subnet4) were captured in the reserach network CESNET3 and annotated using HTTP user-agent, HTTP Host Name, TLS SNI, QUIC SNI, hand annotation, reverse DNS lookup and Shodan. The first subnet (subnet1) contains networks of several small institutions, the other three (subnet2-4) belong to large universities.

Dataset local1 was captured on local private network and was annotated manually with information from DHCP logs and intern clients database.

All data were captured same day in March 2024.

Link to the research paper and citation will be added, when available.

Datasets contents:| Dataset | Flow count | Unique devices | Features | Unique OS ||----------|------------|----------------|----------|-----------|| subnet1 | 2,199,185 | 3605 | 20 | 5 || subnet2 | 5,670,778 | 4520 | 20 | 5 || subnet3 | 4,550,439 | 5736 | 20 | 5 || subnet4 | 2,973,367 | 3803 | 20 | 5 || local1 | 17,310,217 | 984 | 6 | 5 ||----------|------------|----------------|----------|-----------|| Total | 32,703,986 | 18,648 |----------------------------------------------------------------------------Class distribution across the datasets:| Dataset | Android | iOS | Linux | macOS | Windows ||---------|------------|------------|------------|------------|-------------|| subnet1 | 40,738 | 671 | 32,818 | 19,754 | 2,105,203 || subnet2 | 102,554 | 15,517 | 241,682 | 260,246 | 5,050,778 || subnet3 | 266,805 | 10,285 | 109,849 | 295,035 | 3,868,464 || subnet4 | 27,290 | 1,151 | 33,582 | 113,008 | 2,798,335 || local1 | 4,133,253 | 1,703,947 | 177,842 | 1,452,872 | 9,842,302 |----------------------------------------------------------------------------Included features in datasets subnet1, subnet2, subnet3 and subnet4:

| OS_LABEL | OS annotation label || DST_PORT | transport layer destination port || SRC_PORT | transport layer source port || TCP_SYN_SIZE | TCP SYN packet size || TCP_WIN | TCP window size || TCP_WIN_REV | TCP window size || TCP_MSS | TCP maximum segment size || PACKETS | number of packets in data flow (src to dst) || PACKETS_REV | number of packets in data flow (dst to src) || BYTES | number of bytes in data flow (src to dst) || BYTES_REV | number of bytes in data flow (dst to src) || TCP_OPTIONS | TCP options bitfield || TCP_OPTIONS_REV| TCP options bitfield || DIR_BIT_FIELD | bit field for determining outgoing/incoming traffic || FLOW_END_REASON| FlowEndReason [RFC5102] || L3_FLAGS | L3 FLAGS || L3_FLAGS_REV | L3 FLAGS || PROTOCOL | transport protocol || TCP_FLAGS | TCP protocol flags (src to dst) || TCP_FLAGS_REV | TCP protocol flags (dst to src) || TTL | IP TTL field (rounded to nearest higher power of two) || TTL_REV | IP TTL field |----------------------------------------------------------------------------Included features in dataset local1:| OS_LABEL | OS annotation label || SRC_PORT | transport layer source port || TCP_SYN_SIZE | TCP SYN packet size || TCP_WIN | TCP window size || TCP_MSS | TCP maximum segment size || PROTOCOL | transport protocol || TTL | IP TTL field (rounded to nearest higher power of two) |Detailed information about included fields can be found on the website: https://github.com/CESNET/ipfixprobe

----------------------------------------------------------------------------For more information, contact author via email address(hulakmat@fit.cvut.cz).

Mobile Legends: Bang Bang pro players (SEA)

This is an absolute Beginner friendly Dataset to practice your Data Cleaning and Data Visualizations.

Do comment if any Information regarding the datasets are required.

Mobile Legends: Bang Bang aka **MLBB **is an MOBA game available to play on both Android and IOS.

If you are interested in E-Sports data. I would Recommend you to check out https://liquipedia.net/

The Dataset contains all the professional players in South East Asia as of November 1, 2022.

** Key Details**:

• Both the datasets i.e. , Player_details.csv and ML_players.csv can be merged on the index column provided.

Column Description:

ML_players.csv

• Column0(index) : Basically unique numbers or row numbering.
• Players: Name of the players there are known by (IGN, In game name).
• Name: Players real name.
• Team: Professional team the player plays for.

Player_details.csv

• Column0(index) : Basically unique numbers or row numbering.
• Nationality: Country the player belongs to. (Birth country)
• Birth_date: Day the player was born.
• Status: Players current status as an professional player.
• Laner: Role of the player in professional tournaments.
• Earnings: Money earned as a professional player.