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.

Dataset

This dataset was created by Jason

Released under Data files © Original Authors

Contents

Household Saving Rate in the United States increased to 4.90 percent in April from 4.30 percent in March of 2025. This dataset provides - United States Personal Savings Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Success.ai presents an exclusive opportunity to connect directly with top-tier decision-makers in the finance sector through our CEO Contact Data, specifically designed for venture capital and private equity investors based in the USA. This tailored database is part of our expansive collection that draws from over 700 million global profiles, meticulously verified to ensure the highest quality and reliability.

Why Choose Success.ai’s CEO Contact Data?

Specialized Investor Profiles: Access detailed profiles of CEOs and senior executives from leading venture capital and private equity firms across the United States. Investment Insights: Gain valuable insights into investment trends, fund sizes, and sectors of interest directly from the decision-makers. Verified Contact Details: We provide up-to-date email addresses and phone numbers, ensuring that you reach the right people without the hassle of outdated information. Data Features:

Targeted Financial Sector Data: Directly target influential figures in the financial sector who have the authority to make investment decisions. Comprehensive Executive Information: Profiles include not just contact information but also professional backgrounds, areas of investment focus, and operational histories. Geographic Precision: Focus your outreach efforts on US-based investors with our geographically segmented data. Flexible Delivery and Integration: Choose from various delivery options including API access for real-time integration or static files for periodic campaign use, allowing for seamless incorporation into your CRM or marketing automation tools.

Competitive Pricing with Best Price Guarantee: Success.ai is committed to providing competitive pricing without compromising on quality, backed by our Best Price Guarantee.

Effective Use Cases for CEO Contact Data:

Fundraising Initiatives: Connect with venture capital and private equity firms for fundraising activities or financial endorsements. Partnership Development: Forge strategic partnerships and collaborations with leading investors in the industry. Event Invitations: Send personalized invites to investment summits, roundtables, and networking events catered to top financial executives. Market Analysis: Utilize executive insights to better understand the investment landscape and refine your market strategies. Quality Assurance and Compliance:

Rigorous Data Verification: Our data undergoes continuous verification processes to maintain accuracy and completeness. Compliance with Regulations: All data handling practices adhere to GDPR and other relevant data protection laws, ensuring ethical and lawful use. Support and Custom Solutions:

Client Support: Our team is available to assist with any queries or specific data needs you may have. Tailored Data Solutions: Customize data sets according to specific criteria such as investment size, sector focus, or geographic location. Start Connecting with Venture Leaders: Empower your business strategy and network building by accessing Success.ai’s CEO Contact Data for venture capital and private equity investors. Whether you're looking to initiate funding rounds, explore investment opportunities, or engage with top financial leaders, our reliable data will pave the way for meaningful connections and successful outcomes.

Contact Success.ai today to discover how our precise and comprehensive data can transform your business approach and help you achieve your strategic goals.

Foreign Direct Investment in Russia increased by 1823 USD Million in the fourth quarter of 2024. This dataset provides the latest reported value for - Russia Foreign Direct Investment - Net Flows - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Graph and download economic data for Personal Saving Rate (PSAVERT) from Jan 1959 to Apr 2025 about savings, personal, rate, and USA.

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

Dhaka Stock Exchange (DSE) Historical Stock Prices (2000-2025)

Dataset Overview:

This dataset provides a comprehensive historical record of stock prices from the Dhaka Stock Exchange (DSE), the primary stock exchange of Bangladesh. Spanning from January 1, 2000, to February 26, 2025, it offers a detailed look into the daily trading activity of 464 unique stocks.

Key Features:

• Date: The trading date (YYYY-MM-DD format).
• Script (Stock Name): The name or ticker symbol of the listed company.
• Open: The opening price of the stock on the given trading day.
• High: The highest price reached by the stock during the trading day.
• Low: The lowest price reached by the stock during the trading day.
• Close: The closing price of the stock on the given trading day.
• Volume: The total number of shares traded for the stock on the given trading day.

Data Characteristics:

• Time Span: January 1, 2000, to February 26, 2025.
• Number of Unique Stocks: 464
• Frequency: Daily
• Accuracy: Clean and accurate data, suitable for reliable analysis.

Potential Uses:

• Financial Analysis: Analyze stock trends, volatility, and performance over time.
• Machine Learning: Develop predictive models for stock price forecasting.
• Economic Research: Study the impact of economic events on the Bangladeshi stock market.
• Investment Strategies: Backtest trading strategies and identify potential investment opportunities.
• Educational Purposes: Learn about stock market dynamics and data analysis in finance.

Acknowledgements:

This dataset was meticulously compiled and cleaned to provide a valuable resource for researchers, analysts, and investors interested in the Dhaka Stock Exchange.

Note:

While efforts have been made to ensure the accuracy of the data, users are advised to conduct their own due diligence and validation before making any investment decisions based on this dataset.

This description highlights the key aspects of your dataset, its potential uses, and its reliability. Feel free to adjust it further based on any specific details or insights you want to emphasize!

Context

The dataset contains prices and volumes for different stocks

Here is an example: cat 201801_Amsterdam_AALB_NoExpiry.txt

01/02/2018,09:01:00, 42.39, 42.39, 42.21, 42.21, 737 01/02/2018,09:02:00, 42.28, 42.28, 42.27, 42.27, 277 01/02/2018,09:04:00, 42.24, 42.24, 42.24, 42.24, 177 01/02/2018,09:05:00, 42.23, 42.23, 42.22, 42.22, 1543 01/02/2018,09:06:00, 42.23, 42.23, 42.23, 42.23, 241

The dataset contains trading data for 2182 unique stocks, on 40 unique stock exchanges. The monthly data is provided by stocks with each stock being associated with a specific stock exchange and is initially stored in the .txt format. Each file contains a trading history of a stock in a particular month and has the following schema.

Content

The columns correspond to the following schema:

• Date (Calendar Year 2018)
• Time (in CET timezone) The following 5 columns describe the data, that came in until the last minute
• Opening price (The first price within this minute)
• Highest price (The highest price within this minute)
• Lowest price (The lowest price within this minute)
• Closing price (the last price within this minute)
• Sum of the volume of all transactions within this minute

Dataset is a zipped file of stocks from many stock markets and forex. It covers the whole of 2018. Notice the following: 1. All mentioned timestamps are CET. 2. There are missing records and irregularities on the updates – see the previous example. You need to decide how to handle the missing values/records. 3. Different stocks have different update frequencies.

Disclaimer: Educational Purposes Only

The financial and International Securities Identification Number (ISIN) data listed on this platform is provided solely for educational purposes. The information is intended to serve as general guidance and does not constitute financial advice, an endorsement, or a recommendation for the purchase or sale of any securities.

While we strive to ensure the accuracy and timeliness of the information presented, we make no representations or warranties, express or implied, regarding the completeness, accuracy, reliability, suitability, or availability of the provided data. Users are encouraged to independently verify any information obtained from this platform before making any investment decisions.

This platform and its operators are not responsible for any errors, omissions, or inaccuracies in the provided data, nor for any actions taken in reliance on such information. Users are strongly advised to conduct thorough research and seek the advice of qualified financial professionals before making any investment decisions.

The use of International Securities Identification Numbers (ISINs) and other financial data is subject to various regulations and licensing agreements. Users are responsible for complying with all applicable laws and respecting any terms and conditions associated with the use of such data.

By accessing and using this platform, users acknowledge and agree that they are doing so at their own risk and discretion. This educational content is not a substitute for professional financial advice, and users should consult with qualified professionals for specific guidance tailored to their individual circumstances.

Stock Price Prediction

A stock market, equity market or share market is the aggregation of buyers and sellers of stocks (also called shares), which represent ownership claims on businesses; these may include securities listed on a public stock exchange, as well as stock that is only traded privately, such as shares of private companies which are sold to investors through equity crowdfunding platforms.

The secret of a successful stock trader is being able to look into the future of the stocks and make wise decisions. Accurate prediction of stock market returns is a very challenging task due to volatile and non-linear nature of the financial stock markets. With the introduction of artificial intelligence and increased computational capabilities, programmed methods of prediction have proved to be more efficient in predicting stock prices.

Here, you are provided dataset of a public stock market for 104 stocks. Can you forecast the future closing prices for these stocks with your Data Science skills for the next 2 months?

This the Daily FinNews for 6000+ Stocks dataset (forked from Miguel Aenlle). I don't the dataset nor do I claime credit for it. This dataset was preprocessed lightly. I deleted missing values and duplicates (there around 500K duplicates in my case). I only used a few thousand samples for my research. Feel free to use it. If you have suggestions on how preprocess it better or any other comments, please feel free to do so.

This dataset provides a comprehensive collection of historical stock data for all the companies listed on the S&P 500 index. It includes details such as daily open, close, high, low prices, and volume for each listed company. The dataset is intended to help researchers, investors, and data scientists gain insights into the performance of these companies and explore trends, patterns, or anomalies in the stock market.

🥤 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

Perfect for learning how to manage JSON formatted data and practice data cleaning techniques.

Content

• Watch listings from A. Lange & Söhne
• Each entry includes details such as:
  • Watch ID
  • Watch title
  • Model information
  • Technical specifications
  • Price information
  • Condition details
  • Seller information

Key Features

• For A. Lange & Söhne, popular models include:
  • Lange 1
  • 1815
  • Saxonia
• Data has been cleaned to remove columns with more than 40% missing values
• Brand and model information has been extracted and standardized using regex patterns
• Special handling for variations in "Söhne" spelling

Potential Use Cases

• Price analysis and prediction for luxury watches
• Market trend analysis
• Brand comparison studies
• Model popularity analysis
• Investment potential assessment

Techsalerator offers an extensive dataset of End-of-Day Pricing Data for all 30 companies listed on the Damascus Securities Exchange (XDSE) in Syria. This dataset includes the closing prices of equities (stocks), bonds, and indices at the end of each trading session. End-of-day prices are vital pieces of market data that are widely used by investors, traders, and financial institutions to monitor the performance and value of these assets over time.

‍

Top 5 used data fields in the End-of-Day Pricing Dataset for Syria:

1. Equity Closing Price :The closing price of individual company stocks at the end of the trading day.This field provides insights into the final price at which market participants were willing to buy or sell shares of a specific company.

2. Bond Closing Price: The closing price of various fixed-income securities, including government bonds, corporate bonds, and municipal bonds. Bond investors use this field to assess the current market value of their bond holdings.

3. Index Closing Price: The closing value of market indices, such as the Botswana stock market index, at the end of the trading day. These indices track the overall market performance and direction.

4. Equity Ticker Symbol: The unique symbol used to identify individual company stocks. Ticker symbols facilitate efficient trading and data retrieval.

5. Date of Closing Price: The specific trading day for which the closing price is provided. This date is essential for historical analysis and trend monitoring.

‍

Top 5 financial instruments with End-of-Day Pricing Data in Syria:

Damascus Securities Exchange (DSE): The primary stock exchange in Syria, tracking the performance of domestic companies listed on the exchange. It provides insights into the Syrian equity market.

Syrian Pound (SYP): The official currency of Syria, used for transactions and trade within the country. The Syrian Pound has faced significant challenges due to the ongoing conflict and economic instability in the country.

Central Bank of Syria (CBS): The central bank responsible for monetary policy, currency issuance, and regulation of the financial sector in Syria. It plays a crucial role in managing the country's economic challenges.

Syrian Petroleum Company (SPC): A state-owned company responsible for the exploration, production, and export of oil and natural gas. Energy resources are important for Syria's economy, and SPC is a key player in the sector.

Commercial Bank of Syria: One of the major state-owned banks in Syria, providing various financial services to individuals and businesses. Despite challenges, the banking sector remains a vital part of the Syrian economy.

‍

If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Syria, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

‍

Data fields included:

Equity Ticker Symbol Equity Closing Price Bond Ticker Symbol Bond Closing Price Index Ticker Symbol Index Closing Price Date of Closing Price Equity Name Equity Volume Equity High Price Equity Low Price Equity Open Price Bond Name Bond Coupon Rate Bond Maturity Index Name Index Change Index Percent Change Exchange Currency Total Market Capitalization Dividend Yield Price-to-Earnings Ratio (P/E) ‍

Q&A:

1. How much does the End-of-Day Pricing Data cost in Syria ?

The cost of this dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

1. How complete is the End-of-Day Pricing Data coverage in Syria?

Techsalerator provides comprehensive coverage of End-of-Day Pricing Data for various financial instruments, including equities, bonds, and indices. Thedataset encompasses major companies and securities traded on Syria exchanges.

1. How does Techsalerator collect this data?

Techsalerator collects End-of-Day Pricing Data from reliable sources, including stock exchanges, financial news outlets, and other market data providers. Data is carefully curated to ensure accuracy and reliability.

1. Can I select specific financial instruments or multiple countries with Techsalerator's End-of-Day Pricing Data?

Techsalerator offers the flexibility to select specific financial instruments, such as equities, bonds, or indices, depending on your needs. While the dataset focuses on Botswana, Techsalerator also provides data for other countries and international markets.

1. How do I pay for this dataset?

Techsalerator accepts various payment methods, including credit cards, direct transfers, ACH, and wi...

Largest US Venture Funding Deals of 2023

Overview:

This CSV file contains a comprehensive dataset of the largest venture funding deals in the United States for the year 2023. It is a valuable resource for anyone interested in understanding the landscape of venture capital investments during this period.

Contents:

The dataset is structured with the following columns, each providing specific information about the funding deals:

1. Company: The names of the companies that secured funding.
2. Amount: The amount of funding received by each company in USD.
3. Lead investors: The primary investors or firms leading the funding rounds.
4. Valuation: The estimated valuation of the companies at the time of funding.
5. Industry: The industry or sector to which each company belongs.
6. Date reported: The date when the funding deals were reported.

Data Source:

The dataset was compiled from publicly available sources and reports, making it a reliable reference for analysis and research.

Note: Please use this dataset responsibly and respect any terms of use or licensing associated with the data source, if applicable.

Ethereum (ETH-USD) Historical Dataset from 2015 to 2021

Date: Represents the date at which the share is traded in the stock market.

Open: Represents the opening price of the stock at a particular date. It is the price at which a stock started trading when the opening bell rang.

Close: Represents the closing price of the stock at a particular date. It is the last buy-sell order executed between two traders. The closing price is the raw price, which is just the cash value of the last transacted price before the market closes.

High: The high is the highest price at which a stock is traded during a period. Here the period is a day.

Low: The low is the lowest price at which a stock is traded during a period. Here the period is a day.

Adj Close: The adjusted closing price amends a stock's closing price to reflect that stock's value after accounting for any corporate actions. The adjusted closing price factors in corporate actions, such as stock splits, dividends, and rights offerings.

Volume: Volume is the number of shares of security traded during a given period of time. Here the security is stock and the period of time is a day.

Sources: Investopedia

• Date: The date of the stock data.
• Open: The price of the stock at the start of the trading day.
• High: The highest price of the stock during the trading day.
• Low: The lowest price of the stock during the trading day.
• Close: The price of the stock at the end of the trading day.
• Volume: The total number of shares traded during the day.
• Dividends: Any dividend payments made to shareholders on that date.
• Stock Splits: Any stock splits that occurred on that date.

Techsalerator offers an extensive dataset of End-of-Day Pricing Data for all 2113 companies listed on the Libyan Stock Market (XLSM) in Libya. This dataset includes the closing prices of equities (stocks), bonds, and indices at the end of each trading session. End-of-day prices are vital pieces of market data that are widely used by investors, traders, and financial institutions to monitor the performance and value of these assets over time.

‍

Top 5 used data fields in the End-of-Day Pricing Dataset for Libya:

1. Equity Closing Price :The closing price of individual company stocks at the end of the trading day.This field provides insights into the final price at which market participants were willing to buy or sell shares of a specific company.

2. Bond Closing Price: The closing price of various fixed-income securities, including government bonds, corporate bonds, and municipal bonds. Bond investors use this field to assess the current market value of their bond holdings.

3. Index Closing Price: The closing value of market indices, such as the Botswana stock market index, at the end of the trading day. These indices track the overall market performance and direction.

4. Equity Ticker Symbol: The unique symbol used to identify individual company stocks. Ticker symbols facilitate efficient trading and data retrieval.

5. Date of Closing Price: The specific trading day for which the closing price is provided. This date is essential for historical analysis and trend monitoring.

‍

Top 5 financial instruments with End-of-Day Pricing Data in Libya:

Libyan Stock Market Index: The main index that tracks the performance of domestic companies listed on the Libyan Stock Market. This index provides an overview of the overall market performance in Libya.

Foreign Company Index: The index that tracks the performance of foreign companies listed on the Libyan Stock Market. This index reflects the performance of international companies operating in Libya.

Company A: A prominent Libyan company operating in various sectors, such as telecommunications, energy, or banking. This company's stock is widely traded on the Libyan Stock Market.

Company B: A leading financial services provider in Libya, offering banking, insurance, or investment services. This company's stock is actively traded on the Libyan Stock Market.

Company C: A major player in the Libyan agricultural sector, involved in the production and distribution of agricultural products. This company's stock is listed and actively traded on the Libyan Stock Market.

‍

If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Libya, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

‍

Data fields included:

Equity Ticker Symbol Equity Closing Price Bond Ticker Symbol Bond Closing Price Index Ticker Symbol Index Closing Price Date of Closing Price Equity Name Equity Volume Equity High Price Equity Low Price Equity Open Price Bond Name Bond Coupon Rate Bond Maturity Index Name Index Change Index Percent Change Exchange Currency Total Market Capitalization Dividend Yield Price-to-Earnings Ratio (P/E) ‍

Q&A:

1. How much does the End-of-Day Pricing Data cost in Libya ?

The cost of this dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

1. How complete is the End-of-Day Pricing Data coverage in Libya?

Techsalerator provides comprehensive coverage of End-of-Day Pricing Data for various financial instruments, including equities, bonds, and indices. Thedataset encompasses major companies and securities traded on Libya exchanges.

1. How does Techsalerator collect this data?

Techsalerator collects End-of-Day Pricing Data from reliable sources, including stock exchanges, financial news outlets, and other market data providers. Data is carefully curated to ensure accuracy and reliability.

1. Can I select specific financial instruments or multiple countries with Techsalerator's End-of-Day Pricing Data?

Techsalerator offers the flexibility to select specific financial instruments, such as equities, bonds, or indices, depending on your needs. While the dataset focuses on Botswana, Techsalerator also provides data for other countries and international markets.

1. How do I pay for this dataset?

Techsalerator accepts various payment methods, including credit cards, direct transfers, ACH, and wire transfers, facilitating a convenient and secure payment process.

1. How do I receive the data?

‍Techsalerato...