As of October 2025, Google represented ***** percent of the global online search engine referrals on desktop devices. Despite being much ahead of its competitors, this represents a modest increase from the previous months. Meanwhile, its longtime competitor Bing accounted for ***** percent, as tools like Yahoo and Yandex held shares of over **** percent and **** percent respectively. Google and the global search market Ever since the introduction of Google Search in 1997, the company has dominated the search engine market, while the shares of all other tools has been rather lopsided. The majority of Google revenues are generated through advertising. Its parent corporation, Alphabet, was one of the biggest internet companies worldwide as of 2024, with a market capitalization of **** trillion U.S. dollars. The company has also expanded its services to mail, productivity tools, enterprise products, mobile devices, and other ventures. As a result, Google earned one of the highest tech company revenues in 2024 with roughly ****** billion U.S. dollars. Search engine usage in different countries Google is the most frequently used search engine worldwide. But in some countries, its alternatives are leading or competing with it to some extent. As of the last quarter of 2023, more than ** percent of internet users in Russia used Yandex, whereas Google users represented little over ** percent. Meanwhile, Baidu was the most used search engine in China, despite a strong decrease in the percentage of internet users in the country accessing it. In other countries, like Japan and Mexico, people tend to use Yahoo along with Google. By the end of 2024, nearly half of the respondents in Japan said that they had used Yahoo in the past four weeks. In the same year, over ** percent of users in Mexico said they used Yahoo.

In January 2024, Yahoo! Search had a worldwide market share of **** percent. The search engine is powered by Microsoft's Bing. Neither of these web search providers comes close to the dominance of market leader Google.

Title: Stock Prices of 500 Biggest Companies by Market Cap (Last 5 Years)

Description: This dataset comprises historical stock market data extracted from Yahoo Finance, spanning a period of five years. It includes daily records of stock performance metrics for the top 500 companies based on market capitalization.

Attributes: 1. Date: The date corresponding to the recorded stock market data. 2. Open: The opening price of the stock on a given date. 3. High: The highest price of the stock reached during the trading day. 4. Low: The lowest price of the stock observed during the trading day. 5. Close: The closing price of the stock on a specific date. 6. Volume: The volume of shares traded on the given date. 7. Dividends: Any dividend payments made by the company on that date (if applicable). 8. Stock Splits: Information regarding any stock splits occurring on that date. 9. Company: Ticker symbol or identifier representing the respective company.

Usefulness: - Investors and analysts can leverage this dataset to conduct various analyses such as trend analysis, volatility assessment, and predictive modeling. - Researchers can explore correlations between stock prices of different companies, sector-wise performance, and market trends over the specified duration. - Machine learning enthusiasts can employ this dataset for developing predictive models for stock price forecasting or anomaly detection.

Note: Prior to using this dataset, it's recommended to perform data cleaning, handling missing values, and verifying the consistency of data across companies and time periods.

License: The dataset is sourced from Yahoo Finance and is provided for analytical purposes. Refer to Yahoo Finance's terms of use for further details on data usage and licensing.

In February 2025, Microsoft Sites handled **** percent of all search queries in the United States. During the same period, Verizon Media (formerly known as Yahoo and Oath) had a search market share of little less than ** percent. Market leader Google generated **** percent of all core search queries in the United States.

The "yahoo_finance_dataset(2018-2023)" dataset is a financial dataset containing daily stock market data for multiple assets such as equities, ETFs, and indexes. It spans from April 1, 2018 to March 31, 2023, and contains 1257 rows and 7 columns. The data was sourced from Yahoo Finance, and the purpose of the dataset is to provide researchers, analysts, and investors with a comprehensive dataset that they can use to analyze stock market trends, identify patterns, and develop investment strategies. The dataset can be used for various tasks, including stock price prediction, trend analysis, portfolio optimization, and risk management. The dataset is provided in XLSX format, which makes it easy to import into various data analysis tools, including Python, R, and Excel.

The dataset includes the following columns:

Date: The date on which the stock market data was recorded. Open: The opening price of the asset on the given date. High: The highest price of the asset on the given date. Low: The lowest price of the asset on the given date. Close*: The closing price of the asset on the given date. Note that this price does not take into account any after-hours trading that may have occurred after the market officially closed. Adj Close**: The adjusted closing price of the asset on the given date. This price takes into account any dividends, stock splits, or other corporate actions that may have occurred, which can affect the stock price. Volume: The total number of shares of the asset that were traded on the given date.

This datasets specifies the transaction (Buys,Sells,Awards) done between companies and their employess internally. There is no stock exchange data of the public level . As the dataset is collected through public API provided by rapid API platform .

RapidAPI is a comprehensive platform that functions as the world's largest API Hub, serving as a marketplace and management platform for Application Programming Interfaces (APIs). Its primary purpose is to connect developers (API consumers) with a vast array of APIs provided by various developers and companies (API providers).

***key terms :- ***

**Stock Options **:- A stock option is a type of employee benefit that gives you the right to buy company shares at a fixed price usually lower than the market price after a certain period of time.

Restricted Stock Units (RSUs) :- This are the stock grants provided by the company to employees as an compensation which keeps the motivation of the workers high.

Talking about the quality of the dataset , so i had made some filters to their datatype , representation . the size of the dataset is small around 1300 (toy datasets) especially useful and helpful to perform the beginner friendly Exploratory data analysis. There is no primary key for the dataset we need to create an synthetic one .

Columns:-

1. symbol :- It just only shows the ticker symbol of the company's stock.

2. symbolName :- Full Name of the company corresponding to the ticker.

3. fullName :- Name of the company's insider making the transaction.

4. shortJobTitle:- Position of the insider who is making the stock transaction.

5. transactionType:- Type of the transaction ---- Buy, sell & Award.

6. amount :- Number of shares traded in the transaction

7. reportedPrice:- Current price per share reported for the transaction

8. usdValue :- Total amount in dollars for the current transaction.

9. eodHolding :- Insider’s end-of-day holding after the transaction (number of shares remaining).

10. transactionDate:- Date on which transaction has been done.

11. symbolCode:- Type of security traded (e.g., STK for stock, UIT for unit trust).

12. hasOptions:- Indicates if the insider has stock options (Yes/No).

13. symbolType:- Numeric code representing the type of instrument or classification (often internal or system-defined).

Github Link to source code of data collection through API :- https://github.com/Aryan83699/yahoo-stock-exchange

1) name - The full name of the company or stock listed in the dataset.Example: NVIDIA Corporation. dtype -- object

2) symbol - The stock ticker symbol, which is a unique identifier for the company in the stock exchange. Example: NVDA (NVIDIA). dtype -- object

3) price - The current trading price of the stock in USD.Example: 131.29. dtype -- float64

4) change - The net change in the stock price during the last trading session, expressed in USD. Positive values indicate an increase, while negative values indicate a decrease in price. Example: -1.54. dtype -- flaot64

5) volume - The total number of shares traded for the stock during the trading session.Represented in millions (e.g., 197.102M = 197,102,000 shares). Example: 197.102M. dtype -- object

6) market_cap - The market capitalization of the company, calculated as the total number of outstanding shares multiplied by the stock's price.Represented in trillions (T), billions (B), or other notations.Example: 3.202T. dtype -- object

7) pe_ratio - The Price-to-Earnings ratio, a financial metric to evaluate a company's profitability relative to its stock price.A value of -- indicates that the P/E ratio is unavailable, often because the company is not profitable.Example: 44.66. dtype -- float

This statistic shows the market share of search engines in the United States in December 2008 to 2020. In December 2020, Verizon Media's search market share was **** percent, down *** percent from the previous year. The subsidiary was formed in 2017 by Verizon Communications as a merge between newly acquired Yahoo! and AOL.

Our project involves creating a model using Multiple Linear Regression to analyze and predict the stock prices of Pepsico. Multiple Linear Regression is a statistical technique that allows us to understand the relationship between multiple independent variables and a dependent variable, in this case, the stock price of Pepsico. By considering various factors such as historical stock prices, market trends, and financial indicators, we aim to develop a robust model that can provide valuable insights and predictions for investors and analysts. Through the implementation of this model, we hope to uncover meaningful patterns and correlations within the Pepsico share data, enabling more informed decision-making in the dynamic world of stock market investments.

This statistic depicts the market share of non-desktop search held by Yahoo in Italy from ************ to **************. In **************, Yahoo's market share of searching on mobile phone, tablet PC and game consoles was **** percent.

In January 2025, Google represented ***** percent of the UK's mobile search engine market share. Yahoo! ranked second, with **** percent, while all other search hosts also representing less than *** percent each.

This data set includes stock information for the companies Tesla, Porsche, Nio and Ferrari for each day from the date 11/08/2019 to 11/08/2020. Specifically, it shows information about the opening, closing, maximum and minimum price of the session, as well as the volume, the dividends granted to investors and the presence of stock splits generated per day. This dataste has been created with the aim to analyze how the quotes have been evolving during the COVID-19 pandemic in the automotive sector.

The AccionesSectorAutomovil.xlsx dataset contains 4 sheets (TESLA, PAH3.DE, NIO, RACE ) and 9 variables per sheet:

• Fecha: date in dd/MM/yyyy format
• Abrir: value of the share at the market opening expressed in US dollars (USD)
• Max: maximum value of the share throughout the day expressed in USD
• Cierre*: value of the share at the close of the market expressed in USD
• Cierre ajus.*: estimated share value at market close, expressed in USD.
• Volumen: the amount of a specific asset invested in during a day.
• Dividends: money received by shareholders in the form of dividends that day.
• Stock Splits: Whether or not a stock split operation was carried out that day.

For more information about the project visit the link on Github

Overview

This dataset contains historical daily prices for all tickers currently trading on NASDAQ. The up to date list is available from nasdaqtrader.com. The historic data is retrieved from Yahoo finance via yfinance python package.

It contains prices for up to 01 of April 2020. If you need more up to date data, just fork and re-run data collection script also available from Kaggle.

Data Structure

The date for every symbol is saved in CSV format with common fields:

• Date - specifies trading date
• Open - opening price
• High - maximum price during the day
• Low - minimum price during the day
• Close - close price adjusted for splits
• Adj Close - adjusted close price adjusted for both dividends and splits.
• Volume - the number of shares that changed hands during a given day

All that ticker data is then stored in either ETFs or stocks folder, depending on a type. Moreover, each filename is the corresponding ticker symbol. At last, symbols_valid_meta.csv contains some additional metadata for each ticker such as full name.

Context

Microsoft is an American company that develops and distributes software and services such as: a search engine (Bing), cloud solutions and the computer operating system Windows.

Market cap

Market capitalization of Microsoft (MSFT)

Market cap: $3.085 Trillion USD

As of February 2025 Microsoft has a market cap of $3.085 Trillion USD. This makes Microsoft the world's 2nd most valuable company by market cap according to our data. The market capitalization, commonly called market cap, is the total market value of a publicly traded company's outstanding shares and is commonly used to measure how much a company is worth.

Revenue

Revenue for Microsoft (MSFT)
Revenue in 2024 (TTM): $254.19 Billion USD

According to Microsoft's latest financial reports the company's current revenue (TTM ) is $254.19 Billion USD. In 2023 the company made a revenue of $227.58 Billion USD an increase over the revenue in the year 2022 that were of $204.09 Billion USD. The revenue is the total amount of income that a company generates by the sale of goods or services. Unlike with the earnings no expenses are subtracted.

Earnings

Earnings for Microsoft (MSFT)
Earnings in 2024 (TTM): $110.77 Billion USD

According to Microsoft's latest financial reports the company's current earnings are $254.19 Billion USD. In 2023 the company made an earning of $101.21 Billion USD, an increase over its 2022 earnings that were of $82.58 Billion USD. The earnings displayed on this page are the earnings before interest and taxes or simply EBIT.

End of Day market cap according to different sources On Feb 2nd, 2025 the market cap of Microsoft was reported to be:

• $3.085 Trillion USD by Nasdaq

• $3.085 Trillion USD by CompaniesMarketCap

• $3.085 Trillion USD by Yahoo Finance

Content

Geography: USA

Time period: March 1986- February 2025

Unit of analysis: Microsoft Stock Data 2025

Variables

Acknowledgements

This dataset belongs to me. I’m sharing it here for free. You may do with it as you wish.

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Yahoo was the third most popular search engine in India, following Google and Bing. The multinational IT company's share in the South Asian country's desktop search market stood at *** percent as of *************. After years of fluctuation, Yahoo's market share steadily increased, comparable with its share in *************.

MetaData

Microsift Stock Price Data (2010-2024)

Dataset Description

This dataset contains historical stock price data for Microsoft from 2010 to 2024. This data is extracted by using Python's yfinance library and it provides detailed insights into Microsoft stock performance over the years. It includes daily values for the stock's opening and closing prices, adjusted close price, high and low prices, and trading volume. This dataset is ideal for time series analysis, stock trend analysis, and financial machine learning projects such as price prediction models and volatility analysis.

The dataset is extracted from Yahoo Finance

Column Descriptions

Date: The trading date for each entry, in the format.

Adj_Close: Adjusted closing price of Microsoft stock for each trading day, reflecting stock splits, dividends, and other adjustments.

Close: The raw closing price of Microsoft stock at the end of each trading day.

High: The highest price reached by Microsoft stock during the trading day.

Low: The lowest price reached by Microsoft stock during the trading day.

Open: The price of Microsoft stock at the start of the trading day.

Volume: The total number of shares traded during the trading day.

Context

In this dataset you will find several characteristics on global companies listed on the stock exchange. These characteristics are analyzed by millions of investors before they invest their money.

Analyze the stock market performance of thousands of companies ! This is the objective of this dataset !

Content

Among thse charateristics you will find :

• The symbol : The stock symbol is a unique series of letters assigned to a security for trading purposes.
• The shortname : The name of the company
• The sector : The sector of the company (Technology, Financial services, consumer cyclical...)
• The country : The location of the head office.
• The market capitalisation : Market capitalization refers to the total dollar market value of a company's outstanding shares of stock. It is calculated by multiplying the total number of a company's outstanding shares by the current market price of one share.
• The current ratio : The current ratio is a liquidity ratio that measures a company’s ability to pay short-term obligations. A current ratio that is in line with the industry average or slightly higher is generally considered acceptable. A current ratio that is lower than the industry average may indicate a higher risk of distress or default.
• The beta : Beta is a measure of a stock's volatility in relation to the overall market. A beta greater than 1.0 suggests that the stock is more volatile than the broader market, and a beta less than 1.0 indicates a stock with lower volatility.
• The dividend rate : Represents the ratio of a company's annual dividend compared to its share price. (%)

All this data is public data, obtained from the annual financial reports of these companies. They have been retrieved from the Yahoo Finance API and have been checked beforehand.

Inspiration

This dataset has been designed so that it is possible to build a recommendation engine. For example, from an existing position in a portfolio, recommend an alternative with similar characteristics (sector, market capitalization, current ratio,...) but more in line with an investor's expectations (may be with less risk or with more dividends etc...)

If you have question about this dataset you can contact me

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