Data Content

The dataset contains two primary tables:

Revenues Disaggregated by Type: Tracks the performance of key business units, including the core Google advertising segments (Search, YouTube, and Network), the growing Google subscriptions, platforms, and devices segment, the expansive Google Cloud, and the forward-looking Other Bets.

Key Insight: Allows tracking the shift from reliance on traditional search advertising to cloud services and subscription models.

Revenues Disaggregated by Geography: Breaks down total revenue into four major regions: United States, EMEA (Europe, Middle East, and Africa), APAC (Asia-Pacific), and Other Americas.

Key Insight: Provides a clear picture of Google’s global market presence and regional growth dynamics.

Note: All values are reported in millions of U.S. Dollars (USD).

Potential Use Cases and Analysis

Financial Modeling: Build forecasting models for revenue based on historical segment performance.

Segment Analysis: Analyze the contribution and growth rate of Google Cloud versus Google Services over the past decade.

Geographic Trend Analysis: Identify which international markets are driving the fastest revenue growth.

Economic Impact Study: Correlate specific revenue segments with major economic events (e.g., pandemic impact on advertising spend in 2020-2021).

Visualization: Create detailed time-series visualizations of revenue growth across products and regions.

Data Source

The data is sourced directly from the Disaggregated Revenue and Segment Information tables published in Alphabet Inc.'s annual financial filings (10-K reports) from 2016 through 2024.

Overview

Google is ranked in the top 10 with over 73,407 backlinks and a domain score of 94. Google search volume has 83,100,100 a month. So how much money does it take to have an ad of your website?

Content

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2681031%2Fb4d4d101d4c7a560aed2d962a1c1f6de%2Fvolume.PNG?generation=1598539665064163&alt=media" alt="">

Acknowledgements

Thanks for ubersuggest for helping me prove the information for the dataset.

Inspiration

A lot of people have websites that they want to showcase, but at what cost. This also helps people with their DataFrame and csv file skills. Also help people who want to have an Google ad.

📊 Alphabet Inc. (Google) Segment Revenue History (2013-2024)

This dataset provides a comprehensive, standardized, and time-series view of Alphabet Inc.'s (Google's) segment revenues, compiled from various quarterly and annual disclosures (primarily Forms 10-K and Earnings Releases) spanning from 2013 through 2024.

The data has been meticulously cleaned to account for significant shifts in corporate financial reporting structure over the decade, making it immediately useful for longitudinal analysis. All figures are presented in millions of U.S. Dollars.

Dataset Structure & Standardization

The original data categories have been unified into a consistent set of line items to facilitate analysis across all years:

Key Notes on Segment Definitions (Per Google 10-K Disclosures)

This section provides crucial context for interpreting the historical revenue lines, as the definitions of these categories have evolved:

• Advertising Revenue: Revenue from Google Network (formerly "Google Network Members' properties") is primarily generated through AdMob, AdSense, and Google Ad Manager. This revenue is generally reported on a gross basis, meaning the amounts billed to customers are recorded as revenue, and amounts paid to partners (Traffic Acquisition Costs, or TAC) are recorded separately in the cost of revenues.
• Segment Reclassification (2017 Onward): Prior to 2017, advertising revenue was largely grouped under "Google properties" and "Google Network." Beginning in 2017, "Google properties" was split to provide greater detail for "Google Search & other" and "YouTube ads" (reflecting the increasing prominence of YouTube's advertising business).
• Google subscriptions, platforms, and devices: This segment encompasses Alphabet's non-advertising revenues, which includes:
  • Sales of products like Pixel phones and other devices.
  • Revenues from the Google Play Store (app sales, in-app purchases), where revenue is recorded on a net basis as Google acts as an agent facilitating transactions between developers and users.
  • YouTube non-advertising revenues (e.g., YouTube Premium subscriptions and YouTube TV).
• Google Cloud: This segment was explicitly broken out as a separate reporting unit beginning in 2017 to highlight its increasing scale and strategic importance. Revenue prior to 2017 was historically included within the older "Google other revenues" category.
• Other Bets: This segment represents revenue from Alphabet's non-Google ventures (e.g., Waymo, Verily, Fiber).

Potential Analysis

This dataset is ideal for:

• Time-Series Analysis: Tracking the growth rate of core advertising segments versus emerging businesses (Cloud, Subscriptions).
• Strategic Shift Modeling: Analyzing the inflection point (c. 2017) when Google formally split its revenue segments, allowing for pre- and post-split comparisons.
• Segment Weighting: Calculating the proportional contribution of high-growth segments like Google Cloud and YouTube Ads to the Total Revenues over time.
• Trend Forecasting: Projecting future growth trajectories for each segment.

Google Play is the largest app store by number of apps and downloads, accounting for about half of all app downloads in the world. Launched in 2008 as the Android Market, it followed in the footsteps...

IoTeX is a decentralized crypto system, a new generation of blockchain platform for the development of the Internet of things (IoT). The project team is sure that the users do not have such an application that would motivate to implement the technology of the Internet of things in life. And while this will not be created, people will not have the desire to spend money and time on IoT. The developers of IoTeX decided to implement not the application itself, but the platform for creation. It is through the platform that innovative steps in the space of the Internet of things will be encouraged. Learn more... This dataset is one of many crypto datasets that are available within the Google Cloud Public Datasets . As with other Google Cloud public datasets, you can query this dataset for free, up to 1TB/month of free processing, every month. Watch this short video to learn how to get started with the public datasets. Want to know how the data from these blockchains were brought into BigQuery, and learn how to analyze the data? Weitere Informationen

IoTeX is a decentralized crypto system, a new generation of blockchain platform for the development of the Internet of things (IoT). The project team is sure that the users do not have such an application that would motivate to implement the technology of the Internet of things in life. And while this will not be created, people will not have the desire to spend money and time on IoT. The developers of IoTeX decided to implement not the application itself, but the platform for creation. It is through the platform that innovative steps in the space of the Internet of things will be encouraged. Learn more... This dataset is one of many crypto datasets that are available within the Google Cloud Public Datasets . As with other Google Cloud public datasets, you can query this dataset for free, up to 1TB/month of free processing, every month. Watch this short video to learn how to get started with the public datasets. Want to know how the data from these blockchains were brought into BigQuery, and learn how to analyze the data? En savoir plus

IoTeX is a decentralized crypto system, a new generation of blockchain platform for the development of the Internet of things (IoT). The project team is sure that the users do not have such an application that would motivate to implement the technology of the Internet of things in life. And while this will not be created, people will not have the desire to spend money and time on IoT. The developers of IoTeX decided to implement not the application itself, but the platform for creation. It is through the platform that innovative steps in the space of the Internet of things will be encouraged. Learn more... This dataset is one of many crypto datasets that are available within the Google Cloud Public Datasets . As with other Google Cloud public datasets, you can query this dataset for free, up to 1TB/month of free processing, every month. Watch this short video to learn how to get started with the public datasets. Want to know how the data from these blockchains were brought into BigQuery, and learn how to analyze the data? Más información

IoTeX is a decentralized crypto system, a new generation of blockchain platform for the development of the Internet of things (IoT). The project team is sure that the users do not have such an application that would motivate to implement the technology of the Internet of things in life. And while this will not be created, people will not have the desire to spend money and time on IoT. The developers of IoTeX decided to implement not the application itself, but the platform for creation. It is through the platform that innovative steps in the space of the Internet of things will be encouraged. Learn more... This dataset is one of many crypto datasets that are available within the Google Cloud Public Datasets . As with other Google Cloud public datasets, you can query this dataset for free, up to 1TB/month of free processing, every month. Watch this short video to learn how to get started with the public datasets. Want to know how the data from these blockchains were brought into BigQuery, and learn how to analyze the data? 자세히 알아보기

IoTeX is a decentralized crypto system, a new generation of blockchain platform for the development of the Internet of things (IoT). The project team is sure that the users do not have such an application that would motivate to implement the technology of the Internet of things in life. And while this will not be created, people will not have the desire to spend money and time on IoT. The developers of IoTeX decided to implement not the application itself, but the platform for creation. It is through the platform that innovative steps in the space of the Internet of things will be encouraged. Learn more... This dataset is one of many crypto datasets that are available within the Google Cloud Public Datasets . As with other Google Cloud public datasets, you can query this dataset for free, up to 1TB/month of free processing, every month. Watch this short video to learn how to get started with the public datasets. Want to know how the data from these blockchains were brought into BigQuery, and learn how to analyze the data? Scopri di più

IoTeX is a decentralized crypto system, a new generation of blockchain platform for the development of the Internet of things (IoT). The project team is sure that the users do not have such an application that would motivate to implement the technology of the Internet of things in life. And while this will not be created, people will not have the desire to spend money and time on IoT. The developers of IoTeX decided to implement not the application itself, but the platform for creation. It is through the platform that innovative steps in the space of the Internet of things will be encouraged. Learn more... This dataset is one of many crypto datasets that are available within the Google Cloud Public Datasets . As with other Google Cloud public datasets, you can query this dataset for free, up to 1TB/month of free processing, every month. Watch this short video to learn how to get started with the public datasets. Want to know how the data from these blockchains were brought into BigQuery, and learn how to analyze the data? 詳細

This is a dataset of the profits of academic journal publishers. It contains financial information - revenues, profits, and profit marginss - for companies that make this publicly available. The three sheets of the document cover the years 2011, 2012, and 2013. Data is currently incomplete because some publishers do not make this information available, some have not yet for 2013, and some annual reports from 2011 are already difficult to find online. An online verison of this dataset, which may be updated more often, is available at: https://docs.google.com/spreadsheet/ccc?key=0Avt-G30CZ3ZndEtXSVNqTWhCazhSSEhJS3F1bktaaFE&usp=sharing#gid=4 [Accessed 2 May 2014].

In 2025, Meta Platforms generated a revenue of 200.1 billion U.S. dollars, up from 164.5 billion dollars in 2024. However, net income declined by around 3 percent year on year, totaling approximately 60.46 billion dollars. Meta's revenue by segment Meta’s total Family of Apps revenue for 2022 amounted to 114 billion U.S. dollars. Additionally, Meta’s Reality Labs, the company’s VR division, generated around 2.1 billion dollars. Meta’s marketing expenditure for 2022 amounted to just over 15 billion U.S. dollars, up from 14 billion U.S. dollars in the previous year. Increasing audience base despite privacy misgivings Meta’s user numbers have continued to grow steadily throughout past years. In the fourth quarter of 2022, there was a total of 3.74 billion worldwide users across all of Meta’s platforms. For this same time frame, the company recorded 407 million monthly active users across Europe. Downloads of Meta’s app Oculus, for which virtual reality headsets are required, increased greatly from 2020 to 2021, reaching a total of 10.62 million downloads by the end of last year. Up until 2021, downloads had grown in a steady manner but from 2020 to 2021, they more than doubled.User numbers have increased despite data security issues and past controversy such as the Cambridge Analytica scandal in 2018. There remains skepticism surrounding the idea of the metaverse in which Meta aims to immerse itself. Of surveyed adults in the United States, the majority said that they were concerned about their privacy if Meta were to succeed in creating the metaverse.

In 2025, Amazon's net revenue from the subscription services segment amounted to 49.6 billion U.S. dollars. Subscription services include Amazon Prime, for which Amazon reported 230 million paying members worldwide at the end of 2023. The AWS category generated 128.7 billion U.S. dollars in annual sales. During the most recently reported fiscal year, the company’s net revenue amounted to 717 billion U.S. dollars. Amazon revenue segments Amazon is one of the biggest online companies worldwide. In 2019, the company’s revenue increased by 21 percent, compared to Google’s revenue growth during the same fiscal period, which was just 18 percent. The majority of Amazon’s net sales are generated through its North American business segment, which accounted for 236.3 billion U.S. dollars in 2020. The United States are the company’s leading market, followed by Germany and the United Kingdom. Business segment: Amazon Web Services Amazon Web Services, commonly referred to as AWS, is one of the strongest-growing business segments of Amazon. AWS is a cloud computing service that provides individuals, companies and governments with a wide range of computing, networking, storage, database, analytics and application services, among many others. As of the third quarter of 2020, AWS accounted for approximately 32 percent of the global cloud infrastructure services vendor market.

Context

The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website.

Content

The sample dataset contains Google Analytics 360 data from the Google Merchandise Store, a real ecommerce store. The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website. It includes the following kinds of information:

Traffic source data: information about where website visitors originate. This includes data about organic traffic, paid search traffic, display traffic, etc. Content data: information about the behavior of users on the site. This includes the URLs of pages that visitors look at, how they interact with content, etc. Transactional data: information about the transactions that occur on the Google Merchandise Store website.

Fork this kernel to get started.

Acknowledgements

Data from: https://bigquery.cloud.google.com/table/bigquery-public-data:google_analytics_sample.ga_sessions_20170801

Banner Photo by Edho Pratama from Unsplash.

Inspiration

What is the total number of transactions generated per device browser in July 2017?

The real bounce rate is defined as the percentage of visits with a single pageview. What was the real bounce rate per traffic source?

What was the average number of product pageviews for users who made a purchase in July 2017?

What was the average number of product pageviews for users who did not make a purchase in July 2017?

What was the average total transactions per user that made a purchase in July 2017?

What is the average amount of money spent per session in July 2017?

What is the sequence of pages viewed?

It’s hard to get real-world information about what jobs pay, ALISON GREEN published a survey in 2021 on AskAManager.org, a US-centric-ish but does allow for a range of country inputs. The survey is designed to examine payment of different industries based on experience years, field experience years among other variables such as gender, race and education level.

The dataset is “live” and constantly growing, our dataset was downloaded in 23/2/2023.

Data Dictionary

The original dataset includes the following fields: * Age: How old are you? * Industry: What industry do you work in? * Job title: What is your job title? * Extra_job_title: If your job title needs additional context, please clarify here * Annual_salary: "What is your annual salary? If you are part-time or hourly, please enter an annualized equivalent -- what you would earn if you worked the job 40 hours a week, 52 weeks a year.)
* Annual_bonus: How much additional monetary compensation do you get, if any (for example, bonuses or overtime in an average year) only include monetary compensation here, not the value of benefits. * Currency: Please indicate your salary currency. * Other_currency: 'If "Other," please indicate the currency here. * Extra_income_info: "If your income needs additional context, please provide it here. * Work_country: "What country do you work in? * Work_state_US: "If you're in the U.S., what state do you work in? * Work_city: "What city do you work in? * Overall_experience_years: "How many years of professional work experience do you have overall? * Field_experience_years: "How many years of professional work experience do you have in your field?" * Education_level: "What is your highest level of education completed? * Gender: "What is your gender? * Race:"What is your race? (Choose all that apply.)

Quickstart

https://colab.research.google.com/drive/1W6TprjcxOdXsNwswkpm_XX2U_xld9_zZ#offline=true&sandboxMode=true

Context

Predicting the stock market is a game as old as the stock market itself. On popular ML platforms like Kaggle, users often compete to come up with highly nuanced, optimized models to solve the stock market starting just from price data. LSTMs may end up being the most effective model, but the real problem isn't the model - it's the data.

Human and algorithmic traders in the financial industry know this, and augment their datasets with lots of useful information about stocks called "technical indicators". These indicators have fancy sounding names - e.g. the "Aroon Oscillator" and the "Chaikin Money Flow Index", but most boil down to simple calculations involving moving averages and volatility. Access to these indicators is unrestricted for humans (you can view them on most trading platforms), but access to well formatted indicators (csvs instead of visual lines) for large datasets reaching back significantly in time is nearly impossible to find. Even if you pay for a service, API usage limits make putting together such a dataset prohibitively expensive.

The fact that this information is largely kept behind paywalls for large firms with proprietary resources makes me question the fairness of this market. With a data imbalance like this, how can a single trader - a daytrader - expect to make money? I wanted to make this data available to the ML community because it is my hope that bringing this data to the community will help to even the scales. Whether you're just looking to toy around and make a few bucks, or interested in contributing to something larger - a group of people working to develop algorithms to help the "little guy" trade - I hope this dataset will be helpful. To the best of my knowledge, this is the first dataset of its kind, but I hope it is not the last.

Data

• I'd recommend starting here: https://colab.research.google.com/drive/1W6TprjcxOdXsNwswkpm_XX2U_xld9_zZ#offline=true&sandboxMode=true In this notebook, I've uploaded the indicators data and made it available for read access. The notebook will walk you through processing the data and putting it to work building advanced ML models.

• If you'd like to download it directly, you can do so here (31GB): https://drive.google.com/file/d/1HVKWtLWlIZj5yY4T3R1CDSn3c_Nh1lB5/view?usp=sharing

• For legal reasons, I use borismarjanovic's dataset as a baseline, which ends in 2017. If you'd like to update your prices and indicators so your model can trade day to day, I've included instructions and code in the quickstart that should be helpful.

Acknowledgements

• The many online tutorials and specifications which helped me write and test the indicator functions
• borismarjanovic for making public an amazing dataset that I use as a baseline for the colab notebook and the direct download file above
• The many online services that have allowed me to download all the recent price information to augment Boris' dataset (which legally I cannot share, but which helped me develop the infrastructure to update the indicators given new prices data that I share in the quickstart and repo).

Next Steps / Future Directions

• Building inventive models using this dataset to more and more accurately predict stock price movements
• Incorporating arbitrage analysis across stocks
• Hedging
• Options and selling short
• Commodities, currencies, ETFs

Collaboration

If this interests you, reach out! My email is abwilf [at] umich [dot] edu. The repository I used to generate the dataset is here: https://github.com/abwilf/daytrader. I love forks. If you want to work on the project, send me a pull request!

The market size in the 'Computer Vision' segment of the artificial intelligence market worldwide was modeled to amount to 25.92 billion U.S. dollars in 2024. Between 2020 and 2024, the market size rose by 16.3 billion U.S. dollars, though the increase followed an uneven trajectory rather than a consistent upward trend. The market size will steadily rise by 46.74 billion U.S. dollars over the period from 2024 to 2031, reflecting a clear upward trend.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on Computer Vision.

Context:

This synthetic healthcare dataset has been created to serve as a valuable resource for data science, machine learning, and data analysis enthusiasts. It is designed to mimic real-world healthcare data, enabling users to practice, develop, and showcase their data manipulation and analysis skills in the context of the healthcare industry.

Inspiration:

The inspiration behind this dataset is rooted in the need for practical and diverse healthcare data for educational and research purposes. Healthcare data is often sensitive and subject to privacy regulations, making it challenging to access for learning and experimentation. To address this gap, I have leveraged Python's Faker library to generate a dataset that mirrors the structure and attributes commonly found in healthcare records. By providing this synthetic data, I hope to foster innovation, learning, and knowledge sharing in the healthcare analytics domain.

Dataset Information:

Each column provides specific information about the patient, their admission, and the healthcare services provided, making this dataset suitable for various data analysis and modeling tasks in the healthcare domain. Here's a brief explanation of each column in the dataset - - Name: This column represents the name of the patient associated with the healthcare record. - Age: The age of the patient at the time of admission, expressed in years. - Gender: Indicates the gender of the patient, either "Male" or "Female." - Blood Type: The patient's blood type, which can be one of the common blood types (e.g., "A+", "O-", etc.). - Medical Condition: This column specifies the primary medical condition or diagnosis associated with the patient, such as "Diabetes," "Hypertension," "Asthma," and more. - Date of Admission: The date on which the patient was admitted to the healthcare facility. - Doctor: The name of the doctor responsible for the patient's care during their admission. - Hospital: Identifies the healthcare facility or hospital where the patient was admitted. - Insurance Provider: This column indicates the patient's insurance provider, which can be one of several options, including "Aetna," "Blue Cross," "Cigna," "UnitedHealthcare," and "Medicare." - Billing Amount: The amount of money billed for the patient's healthcare services during their admission. This is expressed as a floating-point number. - Room Number: The room number where the patient was accommodated during their admission. - Admission Type: Specifies the type of admission, which can be "Emergency," "Elective," or "Urgent," reflecting the circumstances of the admission. - Discharge Date: The date on which the patient was discharged from the healthcare facility, based on the admission date and a random number of days within a realistic range. - Medication: Identifies a medication prescribed or administered to the patient during their admission. Examples include "Aspirin," "Ibuprofen," "Penicillin," "Paracetamol," and "Lipitor." - Test Results: Describes the results of a medical test conducted during the patient's admission. Possible values include "Normal," "Abnormal," or "Inconclusive," indicating the outcome of the test.

Usage Scenarios:

This dataset can be utilized for a wide range of purposes, including: - Developing and testing healthcare predictive models. - Practicing data cleaning, transformation, and analysis techniques. - Creating data visualizations to gain insights into healthcare trends. - Learning and teaching data science and machine learning concepts in a healthcare context. - You can treat it as a Multi-Class Classification Problem and solve it for Test Results which contains 3 categories(Normal, Abnormal, and Inconclusive).

Acknowledgments:

• I acknowledge the importance of healthcare data privacy and security and emphasize that this dataset is entirely synthetic. It does not contain any real patient information or violate any privacy regulations.
• I hope that this dataset contributes to the advancement of data science and healthcare analytics and inspires new ideas. Feel free to explore, analyze, and share your findings with the Kaggle community.

Image Credit:

Image by BC Y from Pixabay

Link to code

Description:

The **company* that I work for builds iOS & Android mobile applications that are available in the App Store (iOS) and on Google Play (Android). I am a 'data analyst' at this company and am responsible for guiding the software developers in making data-driven decisions in regards to which apps they should build.

**This project was completed as part of a DataQuest course and was not used for a real company.*

Plan:

The criteria that my company has laid out for a successful app can be determined as follows:

• Create a minimal Android version of the application and add it to Google Play.
• The app will be developed further IF it gets a good response from users.
• If app continues to be profitable after 6 months, an iOS version will be built and added to the App store.

The applications my company builds are all free for users to download and install. Our revenue mainly comes from in-app ads, so the number of users for any given app directly influences our profit.

Goal:

The main goal for this project is to analyze data and give our developers more insight on which kind of apps are more likely to attract users.

Conclusion:

Throughout this project, I analyzed data for the mobile apps in the App Store and Google Play in order to understand which apps would be profitable for both markets. I concluded that turning a popular book into an app could become profitable for both Google Play and the App Store. The team might include an audible version of the book, trivia, in-app platform to discuss with other users, daily quotes and more within the app.

The two .csv files for analysis: App Store Google PlayStore

IMPORTANT NOTE

This dataset contains mostly indonesian reviews

Overview

Livin' by Mandiri is a digital financial service platform developed by Bank Mandiri, one of the largest banks in Indonesia. The platform is designed to provide users with a range of financial services and features, including the ability to make payments, transfer money, and manage their finances on their mobile devices. Livin' by Mandiri is available as a mobile app for both Android and iOS devices.

Data source

This dataset collected by scraping reviews on Google Play Store

Usage ideas

EDA and Sentiment Analysis