The dataset contains a total of 25,161 rows, each row representing the stock market data for a specific company on a given date. The information collected through web scraping from www.nasdaq.com includes the stock prices and trading volumes for the companies listed, such as Apple, Starbucks, Microsoft, Cisco Systems, Qualcomm, Meta, Amazon.com, Tesla, Advanced Micro Devices, and Netflix.

Data Analysis Tasks:

1) Exploratory Data Analysis (EDA): Analyze the distribution of stock prices and volumes for each company over time. Visualize trends, seasonality, and patterns in the stock market data using line charts, bar plots, and heatmaps.

2)Correlation Analysis: Investigate the correlations between the closing prices of different companies to identify potential relationships. Calculate correlation coefficients and visualize correlation matrices.

3)Top Performers Identification: Identify the top-performing companies based on their stock price growth and trading volumes over a specific time period.

4)Market Sentiment Analysis: Perform sentiment analysis using Natural Language Processing (NLP) techniques on news headlines related to each company. Determine whether positive or negative news impacts the stock prices and volumes.

5)Volatility Analysis: Calculate the volatility of each company's stock prices using metrics like Standard Deviation or Bollinger Bands. Analyze how volatile stocks are in comparison to others.

Machine Learning Tasks:

1)Stock Price Prediction: Use time-series forecasting models like ARIMA, SARIMA, or Prophet to predict future stock prices for a particular company. Evaluate the models' performance using metrics like Mean Squared Error (MSE) or Root Mean Squared Error (RMSE).

2)Classification of Stock Movements: Create a binary classification model to predict whether a stock will rise or fall on the next trading day. Utilize features like historical price changes, volumes, and technical indicators for the predictions. Implement classifiers such as Logistic Regression, Random Forest, or Support Vector Machines (SVM).

3)Clustering Analysis: Cluster companies based on their historical stock performance using unsupervised learning algorithms like K-means clustering. Explore if companies with similar stock price patterns belong to specific industry sectors.

4)Anomaly Detection: Detect anomalies in stock prices or trading volumes that deviate significantly from the historical trends. Use techniques like Isolation Forest or One-Class SVM for anomaly detection.

5)Reinforcement Learning for Portfolio Optimization: Formulate the stock market data as a reinforcement learning problem to optimize a portfolio's performance. Apply algorithms like Q-Learning or Deep Q-Networks (DQN) to learn the optimal trading strategy.

The dataset provided on Kaggle, titled "Stock Market Stars: Historical Data of Top 10 Companies," is intended for learning purposes only. The data has been gathered from public sources, specifically from web scraping www.nasdaq.com, and is presented in good faith to facilitate educational and research endeavors related to stock market analysis and data science.

It is essential to acknowledge that while we have taken reasonable measures to ensure the accuracy and reliability of the data, we do not guarantee its completeness or correctness. The information provided in this dataset may contain errors, inaccuracies, or omissions. Users are advised to use this dataset at their own risk and are responsible for verifying the data's integrity for their specific applications.

This dataset is not intended for any commercial or legal use, and any reliance on the data for financial or investment decisions is not recommended. We disclaim any responsibility or liability for any damages, losses, or consequences arising from the use of this dataset.

By accessing and utilizing this dataset on Kaggle, you agree to abide by these terms and conditions and understand that it is solely intended for educational and research purposes.

Please note that the dataset's contents, including the stock market data and company names, are subject to copyright and other proprietary rights of the respective sources. Users are advised to adhere to all applicable laws and regulations related to data usage, intellectual property, and any other relevant legal obligations.

In summary, this dataset is provided "as is" for learning purposes, without any warranties or guarantees, and users should exercise due diligence and judgment when using the data for any purpose.

The main stock market index of United States, the US500, rose to 6818 points on December 2, 2025, gaining 0.08% from the previous session. Over the past month, the index has declined 0.50%, though it remains 12.70% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on December of 2025.

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.

“The people who are crazy enough to think they can predict the market... are the ones who do.”

Here’s to the crazy ones—the data dreamers, the analysts, the visionaries who believe that a handful of numbers can reveal the DNA of innovation. This dataset is more than a collection of Apple Inc.’s historical stock prices; it’s a chronicle of invention, perseverance, and thinking differently.

What’s Inside

• Time Span: Daily stock price data for Apple Inc. over multiple years
• Features:
  • Date: The day of the record
  • Open: Price at market open
  • High: Highest price of the day
  • Low: Lowest price of the day
  • Close: Price at market close
  • Volume: Number of shares traded
• Format: CSV, clean and ready for analysis

Why This Matters

Apple is not just a company, it’s a movement. Its stock price reflects not only financial performance, but the world’s response to innovation—launches, leadership changes, economic cycles, and the occasional “one more thing.”

Possibilities

• Visualize long-term growth and volatility
• Model trends, moving averages, or momentum
• Forecast future prices with machine learning
• Detect the impact of major product launches or events
• Explore relationships between volume and price action

Inspiration

As you explore this data, don’t just look for patterns—look for stories. See how moments of genius and risk-taking ripple through the numbers. Use this dataset to inspire your own creativity, your own analysis, your own ‘insanely great’ discoveries.

Whether you’re here to build a predictive model, craft beautiful visualizations, or simply marvel at the journey, remember:
The people who are crazy enough to think they can change the world with data… are the ones who do.

🥤 Coca-Cola (KO) Stock Price History (1919–2025)

This dataset provides daily historical stock price data for The Coca-Cola Company (ticker: KO) from January 2, 1962 to April 6, 2025. It captures Coca-Cola’s stock performance through decades of economic cycles, technological shifts, and global events — making it a rich resource for time-series analysis, investment research, and machine learning projects.

📂 Dataset Overview

🧮 Dataset Dimensions

• Total Rows: 15,922
• Total Columns: 7
• Missing Values: None ✅
• Date Range: 1962-01-02 to 2025-04-06

📊 Summary Statistics

• Highest Close Price: $73.18
• Lowest Close Price: $0.19
• Max Volume: 124M+ shares
• Average Close Price: ~$18.45
• Adjusted Prices: Range from $0.03 to $73.18

💡 Use Cases

• Time-series forecasting with LSTM, ARIMA, Prophet
• Volatility analysis and pattern detection
• Financial data visualization across decades
• Backtesting trading strategies on long-term data
• Comparing adjusted vs. raw stock prices

🧠 Project Ideas

• Predict future stock prices using ML models
• Visualize price trends during major economic events
• Analyze the effect of dividends and stock splits
• Build a financial dashboard using Plotly or Streamlit

📎 License

This dataset is for educational and research purposes only. For financial trading or commercial use, always consult a licensed data provider.

🙌 Acknowledgment

This dataset was compiled to support learning in data science, finance, and AI fields. Feel free to use it in your projects — and if you do, share your work! 📬 Contect info:

You can contect me for more data sets any type of data you want.

-E_mail

-Linkdin

-Kaggle

-X

-Github

Indonesia's main stock market index, the JCI, rose to 8617 points on December 2, 2025, gaining 0.80% from the previous session. Over the past month, the index has climbed 4.13% and is up 19.75% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Indonesia. Indonesia Stock Market (JCI) - values, historical data, forecasts and news - updated on December of 2025.

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.

Stock Price Time Series for .

Stock Price Time Series for Gyldendal A/S. Gyldendal A/S publishes and sells books in Denmark and Norway. The company publishes fiction books, including novels, entertainment literature, crime, classics, poems, short stories, children's, young adult, romance and eroticism, and other fiction books; and non-fiction books, such as biographies, history, society and politics, cook, sports, health and exercise, spirituality, business, art, culture and design, garden, hobbies and crafts, nature, science, and other fiction books. It also provides tutoring services; produces digital teaching materials; and operates reading club. Gyldendal A/S was founded in 1770 and is based in Copenhagen, Denmark.

Stock Price Time Series for Equifax Inc. Equifax Inc. operates as a data, analytics, and technology company. The company operates through three segments: Workforce Solutions, U.S. Information Solutions (USIS), and International. The Workforce Solutions segment offers services that enables customers to verify income, employment, educational history, criminal justice data, healthcare professional licensure, and sanctions of people in the United States; and employer customers with services that that assist them in complying with and automating certain payroll-related and human resource management processes throughout the entire cycle of the employment relationship. The U.S. Information Solutions segment provides consumer and commercial information services, such as credit information and credit scoring, credit modeling and portfolio analytics, locate, fraud detection and prevention, identity verification, and other consulting services; mortgage services; financial marketing services; identity management services; and credit monitoring products. The International segment offers information service products, which include consumer and commercial services, such as credit and financial information, and credit scoring and modeling; and credit and other marketing products and services, as well as offers information, technology, and services to support debt collections and recovery management. It also provides information solutions for businesses, governments and consumers; and human resources business process automation and outsourcing services for employers.It operates in Argentina, Australia, Brazil, Canada, Chile, Costa Rica, Dominican Republic, Ecuador, El Salvador, Honduras, India, Ireland, Mexico, New Zealand, Paraguay, Peru, Portugal, Spain, the United Kingdom, Uruguay, and the United States. The company was founded in 1899 and is headquartered in Atlanta, Georgia.

Revelyst stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

Stock Price Time Series for Daily Journal Corp. Daily Journal Corporation publishes newspapers and websites covering in California, Arizona, Utah, and Australia. It operates in two segments, Traditional Business and Journal Technologies. The company publishes 10 newspapers of general circulation, including Los Angeles Daily Journal, San Francisco Daily Journal, Daily Commerce, The Daily Recorder, The Inter-City Express, San Jose Post-Record, Orange County Reporter, Business Journal, The Daily Transcript, and The Record Reporter. It also provides specialized information services; and serves as an advertising and newspaper representative for commercial and public notice advertising. In addition, the company offers case management software systems and related products, including eCourt, eProsecutor, eDefender, and eProbation, which are browser-based case processing systems; eFile, a browser-based interface that allows attorneys and the public to electronically file documents with the court; and ePayIt, a service primarily for the online payment of traffic citations. It provides its software systems and related products to courts; prosecutor and public defender offices; probation departments; and other justice agencies, including administrative law organizations, city and county governments, and bar associations to manage cases and information electronically, to interface with other justice partners, and to extend electronic services to bar members and the public in 32 states and internationally. Daily Journal Corporation was incorporated in 1987 and is based in Los Angeles, California.

Japan's main stock market index, the JP225, rose to 49553 points on December 2, 2025, gaining 0.51% from the previous session. Over the past month, the index has declined 3.78%, though it remains 26.25% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Japan. Japan Stock Market Index (JP225) - values, historical data, forecasts and news - updated on December of 2025.

Stock Price Time Series for System Support Inc. System Support Holdings Inc. provides various information technology (IT) services in Japan and internationally. It offers Tate Yakusha, a construction work information management system; Shugyo Yakusha, an attendance/work management system; SHIFTEE, a cloud-based shift management system; Voicetant Writer and Voicetant Recorder, which enables voice operation/input; SmartDWH, a cloud-based DWH solution; PinMap that offers customer information mapping service; MOS, a mobile order reception system; PC Keneki Kenchikun, a security check system for safely connecting external devices; ADDPLAT, a data analysis platform; and Smart Rabbit, a food inventory management system for restaurants. The company also provides system consulting/direction, system integration, IT task support, and outsourcing services, as well as ERP, Oracle, cloud, ServiceNow, RPA, infrastructure, virtualization, and workflow solutions. The company was formerly known as System Support Inc. and changed its name to System Support Holdings Inc. in January 2025. System Support Holdings Inc. was incorporated in 1980 and is headquartered in Kanazawa, Japan.

Stock Price Time Series for First Watch Restaurant Group Inc. First Watch Restaurant Group, Inc., through its subsidiaries, operates and franchises restaurants under the First Watch trade name in the United States. The company was formerly known as AI Fresh Super Holdco, Inc. and changed its name to First Watch Restaurant Group, Inc. in December 2019. The company was founded in 1983 and is based in Bradenton, Florida.

Stock Price Time Series for Snowflake Inc.. Snowflake Inc. provides a cloud-based data platform for various organizations in the United States and internationally. The company's platform includes artificial intelligence (AI) Data Cloud, which enables customers to consolidate data into a single source of truth to drive meaningful business insights, build data applications, and share data and data products, as well as applies AI for solving business problems. It serves financial services, advertising, media and entertainment, retail and consumer goods, healthcare and life sciences, manufacturing, technology, telecom, travel and hospitality, and government and defense industries, as well as the public sector. The company was formerly known as Snowflake Computing, Inc. and changed its name to Snowflake Inc. in April 2019. Snowflake Inc. was incorporated in 2012 and is based in Bozeman, Montana.

Stock Price Time Series for Shake Shack Inc. Shake Shack Inc. owns, operates, and licenses Shake Shack restaurants (Shacks) in the United States and internationally. Its Shacks offer burgers, chicken, hot dogs, crinkle cut fries, shakes, frozen custard, beer, wine, and other products. The company was founded in 2001 and is based in New York, New York.

View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

Stock Price Time Series for Box Inc. Box, Inc. provides a cloud content management platform that enables organizations of various sizes to manage and share their content from anywhere on any device in the United States and Japan. The company's Software-as-a-Service platform enables users to work with their content as they need from secure external collaboration, workspaces to e-signature processes, and content workflows improving employee productivity and accelerating business processes. It also offers web, mobile, and desktop applications of its solutions on a platform, as well as the ability to develop custom applications. The company was formerly known as Box.net, Inc. and changed its name to Box, Inc. in November 2011. Box, Inc. was incorporated in 2005 and is headquartered in Redwood City, California.

Nexstar Broadcasting stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.